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Search Results (1,183)

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22 pages, 7689 KiB  
Article
Guidance Gyro System with Two Gimbals and Magnetic Suspension Gyros Using Adaptive-Type Control Laws
by Romulus Lungu, Constantin-Adrian Mihai and Alexandru-Nicolae Tudosie
Micromachines 2025, 16(3), 245; https://doi.org/10.3390/mi16030245 - 20 Feb 2025
Abstract
The authors have designed a structure for a gyro system (used for the guidance of self-guided missiles) with two gimbals and a rotor in magnetic suspension (AMBs—active magnetic bearings). The system (double-gimbal magnetic suspension gyro system for guidance—DGMSGG) orients the common axis rotor [...] Read more.
The authors have designed a structure for a gyro system (used for the guidance of self-guided missiles) with two gimbals and a rotor in magnetic suspension (AMBs—active magnetic bearings). The system (double-gimbal magnetic suspension gyro system for guidance—DGMSGG) orients the common axis rotor AMB (the sight line) in the direction of the target line (the guide line) by means of some control system of the gyro rotor’s rotations and translations, as well as by means of some servo systems for the gimbals’ rotation angle control. The DGMSGG provides specific signals for the missile’s autopilot, to guide it toward the target, so that the guidance line translates parallel to itself to the point of interception of the target (according to the self-guidance method by parallel approach). Based on the DGMSGG’s established mathematical model, the authors propose and design adaptive control systems for the decoupled dynamics of the gyro rotor’s translations and rotations and of the gimbals’ rotations; the concept of dynamic inversion is used, as well as linear dynamic compensators (P.D.- and P.I.D.-type), state observers, reference models, and neural networks. The theoretical results are validated through numerical simulations, using Simulink/Matlab models’ stabilization and orientation operating regimes. Full article
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Figure 1
<p>Frames related to the dynamic components of DGMSGG, rotation angles, angular rates, and correction moments. (<b>a</b>) DGMSGG’s architecture; (<b>b</b>) gyroscopic rotor’s centering; (<b>c</b>) overlapping the line of sight over the guidance line.</p>
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<p>Subsystems for automatic control of gyroscopic rotor’s dynamics (<b>a</b>) translation dynamics control; (<b>b</b>) rotation dynamics control.</p>
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<p>Automatic control system of gyroscopic gimbals’ nonlinear dynamics; (<b>a</b>) the complete block diagram; (<b>b</b>) linear subsystem’s block diagram.</p>
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<p>Dynamic characteristics of the DGMSGG in stabilization mode: (<b>a</b>) of the subsystem in <a href="#micromachines-16-00245-f002" class="html-fig">Figure 2</a>a; (<b>b</b>) of the subsystem in <a href="#micromachines-16-00245-f002" class="html-fig">Figure 2</a>b; (<b>c</b>) of the subsystem in <a href="#micromachines-16-00245-f003" class="html-fig">Figure 3</a>a.</p>
Full article ">Figure 4 Cont.
<p>Dynamic characteristics of the DGMSGG in stabilization mode: (<b>a</b>) of the subsystem in <a href="#micromachines-16-00245-f002" class="html-fig">Figure 2</a>a; (<b>b</b>) of the subsystem in <a href="#micromachines-16-00245-f002" class="html-fig">Figure 2</a>b; (<b>c</b>) of the subsystem in <a href="#micromachines-16-00245-f003" class="html-fig">Figure 3</a>a.</p>
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<p>Dynamic characteristics of the DGMSGG in guidance (orientation) mode: (<b>a</b>) of the subsystem in <a href="#micromachines-16-00245-f002" class="html-fig">Figure 2</a>a; (<b>b</b>) of the subsystem in <a href="#micromachines-16-00245-f002" class="html-fig">Figure 2</a>b; (<b>c</b>) of the subsystem in <a href="#micromachines-16-00245-f003" class="html-fig">Figure 3</a>a.</p>
Full article ">Figure 5 Cont.
<p>Dynamic characteristics of the DGMSGG in guidance (orientation) mode: (<b>a</b>) of the subsystem in <a href="#micromachines-16-00245-f002" class="html-fig">Figure 2</a>a; (<b>b</b>) of the subsystem in <a href="#micromachines-16-00245-f002" class="html-fig">Figure 2</a>b; (<b>c</b>) of the subsystem in <a href="#micromachines-16-00245-f003" class="html-fig">Figure 3</a>a.</p>
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18 pages, 5366 KiB  
Article
Regenerative Structural Fatigue Testing with Digital Displacement Pump/Motors
by Win Rampen, Marek J. Munko, Sergio Lopez Dubon and Fergus Cuthill
Actuators 2025, 14(3), 103; https://doi.org/10.3390/act14030103 - 20 Feb 2025
Abstract
Historically, a large fraction of fatigue testing of both components and structures has been performed using hydraulic actuators. These are typically driven by servo-valves, which are in themselves very inefficient. But, as most tests involve elastically stressing mechanical components, a lot of stored [...] Read more.
Historically, a large fraction of fatigue testing of both components and structures has been performed using hydraulic actuators. These are typically driven by servo-valves, which are in themselves very inefficient. But, as most tests involve elastically stressing mechanical components, a lot of stored energy could be recovered. Unfortunately, servo-valves are not regenerative—simply metering out fluid in order to relax the system prior to the start of the next cycle. There is much to be gained with a more intelligently controlled system. The FastBlade facility in Scotland uses a new type of regenerative test hydraulics. Digital displacement pump/motors (DDPMs), originated by Artemis Intelligent Power, now Danfoss Scotland, are used to load and unload the test structure directly via hydraulic rams. The DDPMs are driven by induction motors supplied by three-phase frequency converters, each with a very loose speed correction target, such that they can speed up or slow down according to the instantaneous torque exerted by the load. The rotating assembly of the induction motor and DDPM is designed to have sufficient inertia so as to function as a kinetic energy storage flywheel. The loading energy is then cyclically transferred between the rotating inertia of the motor/DDPM and the spring energy in the test structure. The electric motor provides sufficient energy to maintain the target average cyclical shaft speed of the DDPM whilst the bulk of the system energy oscillates between the two storage mechanisms. Initial tests (at low load) suggest that this technique requires only 30% of the energy previously needed. FastBlade is a unique facility built by the University of Edinburgh and Babcock, with support from the UK EPSRC, conceived as a means of testing and certifying turbine blades for marine current turbines. However, this approach can be used in any cyclical application where elastic energy is stored. Full article
(This article belongs to the Special Issue Actuation and Control in Digital Fluid Power)
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<p>Simplified schematic of a regenerative hydraulic loading circuit with reversing energy flows.</p>
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<p>Cross-section of an early DDP, illustrating basic conformation and components.</p>
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<p>Rendering of loading frame, jacking actuators, strong wall, and test specimen at FastBlade.</p>
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<p>Photo of the actual FastBlade facility with a short specimen blade on the test.</p>
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<p>Photo of the undercroft where the four Artemis/Danfoss DDPMs are housed. The main level decking can be seen at the top of the photo, and the side of the loading frame is visible on the extreme right.</p>
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<p>A simplified schematic of the hydraulic loading system, consisting of an oil reservoir, a DDPM unit, an adjustable pressure compensated flow control valve, a loading jack, and a load cell.</p>
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<p>Traces of both the flywheel speed and the desired load acting on the specimen specimen deflection showing the energy exchange, both in amplitude and phase. The two vertical lines represent the phase shift, showing the maximum flywheel speed to be slightly leading to the minimum displacement.</p>
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<p>Regeneration test, where power to the VLT is cut at 5 s. (<b>a</b>) The loading cycle continues, slightly diminishing in amplitude cycle by cycle until transferred energy sinks to 1/3rd of the initial amount after 25 cycles. (<b>b</b>) Motor speed slows after power is cut at 5 s. If loading is also stopped, then the system runs down following the smooth red trace. If loading continues, then the speed reduction is slightly faster. The small difference in speed decay illustrates the high round-trip efficiency of the loading system. (<b>c</b>) The energy that is being cycled between the flywheel and load also diminishes with time, but not in a way that might be recognised as a logarithmic decrement. For the first 3 s, after power is cut to the VLT, loading energy remains relatively constant since the flywheel can source the required energy in the time available. As flywheel speed drops, the energy that can be transferred also diminishes.</p>
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<p>The relation between the power losses and the angular velocity of a DDPM for four operating modes: low pressure (LP), medium pressure (MP), high pressure (HP), and idle.</p>
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<p>Sankey diagram for the loading portion of the cycle. It can be seen from the change in speed during the loading cycle that 2.2 kJ given up by the flywheel during its deceleration are passed through to the strain energy in the specimen, and the VLT makes up the rest, as well as supplying the various parasitic losses in the circuit.</p>
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<p>Sankey diagram for the unloading phase of the cycle. Here, 3.37 kJ of energy are given up by the relaxing test specimen and directed to help accelerate the flywheel. The VLT once again makes up for losses.</p>
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<p>Relative cycle input energy for the FastBlade DDPM regenerative system compared to a calculated approximation for a conventional proportional valve with a variable pump and accumulator supply.</p>
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21 pages, 8496 KiB  
Article
Study on Spatiotemporal Variation in Internal Temperature Field in Quartz Flexible Accelerometer
by Zhigang Zhang, Fangxiang Tang, Ziwei Zhao, Zhao Zhang and Lijun Tang
Micromachines 2025, 16(2), 241; https://doi.org/10.3390/mi16020241 - 19 Feb 2025
Abstract
Quartz flexible accelerometers (QFAs) are a type of temperature-sensitive sensor, whereby a change in temperature will cause the key parameters of the accelerometer to drift and cause stability errors. Due to the absence of effective methods for sensing the temperature of internal accelerometer [...] Read more.
Quartz flexible accelerometers (QFAs) are a type of temperature-sensitive sensor, whereby a change in temperature will cause the key parameters of the accelerometer to drift and cause stability errors. Due to the absence of effective methods for sensing the temperature of internal accelerometer components, existing temperature error correction approaches primarily rely on shell temperature measurements to establish correction models. Consequently, most correction methods achieve higher accuracy during the steady-state heat conduction phase of the accelerometer, whereas the correction error markedly increases during the transient heat conduction phase. To elucidate the temperature discrepancy between the QFA shell and its internal components and to support the development of a temperature error correction method for QFAs based on the internal temperature as a reference, this paper investigated the heat exchange dynamics between the interior and exterior of a QFA. A thermal conduction simulation model of the QFA was established, from which the spatiotemporal distribution patterns of the internal temperature field were derived. The results indicate that the temperature of the QFA shell changes significantly faster than that of the internal meter head in the early stage of the temperature change. The temperature gradient between the shell and the meter head first increases and then decreases, and the rate of temperature change in the upper part of the accelerometer is faster than that in the lower part. Before thermal equilibrium is reached, the temperature distribution inside the accelerometer is uneven in terms of time and space. Inside the accelerometer, the yoke iron, swing plate assembly, servo circuit, and magnetic steel assembly are the main components that exhibit differences in the internal temperature change in the QFA. When developing the temperature error correction method, it was crucial to address and mitigate the impact of temperature variations among these components. The average RMSE between the predicted temperature from the heat transfer model established in this paper and the experimental results was 0.4 °C. This indicates that the model can accurately predict the temperature variation within the QFA, thereby providing robust support for investigating the temperature behavior inside the QFA and offering essential technical foundations for enhancing the accuracy of the temperature error correction method. Full article
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<p>Procedure of the conventional temperature error correction method for QFAs.</p>
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<p>Experimental results of the polynomial temperature correction model.</p>
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<p>Structure of QFA.</p>
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<p>Closed-loop system structure of QFA.</p>
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<p>Structure of the closed-loop system for the QFA incorporating the error term.</p>
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<p>The numerical indication on the external surface of the QFA.</p>
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<p>Heat transfer simulation model of QFA.</p>
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<p>Simulation results of the transient temperature field within the QFA as the ambient temperature increased from −4.3 °C to 17.3 °C. (<b>a</b>) Simulation results at 0 s; (<b>b</b>) Simulation results at 30 s; (<b>c</b>) Simulation results at 60 s; (<b>d</b>) Simulation results at 600 s; (<b>e</b>) Simulation results at 1800 s; (<b>f</b>) Simulation results at 3600 s.</p>
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<p>Simulation results of the transient temperature field within the QFA as the ambient temperature decreased from 17.3 °C to −9.7 °C. (<b>a</b>) Simulation results at 0 s; (<b>b</b>) Simulation results at 30 s; (<b>c</b>) Simulation results at 60 s; (<b>d</b>) Simulation results at 600 s; (<b>e</b>) Simulation results at 1800 s; (<b>f</b>) Simulation results at 3600 s.</p>
Full article ">Figure 9 Cont.
<p>Simulation results of the transient temperature field within the QFA as the ambient temperature decreased from 17.3 °C to −9.7 °C. (<b>a</b>) Simulation results at 0 s; (<b>b</b>) Simulation results at 30 s; (<b>c</b>) Simulation results at 60 s; (<b>d</b>) Simulation results at 600 s; (<b>e</b>) Simulation results at 1800 s; (<b>f</b>) Simulation results at 3600 s.</p>
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<p>Temperature differential between internal components and shell in response to ambient temperature variations. (<b>a</b>) Results observed when ambient temperature rose from −4.3 °C to 17.3 °C; (<b>b</b>) results observed when ambient temperature dropped from 17.3 °C to −9.7 °C.</p>
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<p>Temperature testing platform for QFA. (<b>a</b>) The structure of the temperature testing platform; (<b>b</b>) a physical map of the temperature testing platform.</p>
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<p>The variation curves of the temperature of the shell <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>s</mi> <mi>h</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math> and the upper surface temperature of the meter head <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>M</mi> <mi>H</mi> </mrow> </msub> </mrow> </semantics></math> when the temperature changed. (<b>a</b>) Results observed when the ambient temperature rose from −4.3 °C to 17.3 °C; (<b>b</b>) results observed when the ambient temperature dropped from 17.3 °C to −9.7 °C.</p>
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<p>The variation curves of the temperature of the shell <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>s</mi> <mi>h</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math> and the upper surface temperature of the meter head <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>M</mi> <mi>H</mi> </mrow> </msub> </mrow> </semantics></math> when the temperature changed. (<b>a</b>) Results observed when the ambient temperature rose from −4.3 °C to 17.3 °C; (<b>b</b>) results observed when the ambient temperature dropped from 17.3 °C to −9.7 °C.</p>
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<p>Comparison of simulation results and experimental results for temperature variation curves of <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>s</mi> <mi>h</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>M</mi> <mi>H</mi> </mrow> </msub> </mrow> </semantics></math> under ambient temperature changes. (<b>a</b>) Comparison of experimental and simulation values of <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>s</mi> <mi>h</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math> as ambient temperature increased from −4.3 °C to 17.3 °C; (<b>b</b>) comparison of experimental and simulation values of <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>M</mi> <mi>H</mi> </mrow> </msub> </mrow> </semantics></math> as ambient temperature increased from −4.3 °C to 17.3 °C; (<b>c</b>) comparison of experimental and simulation values of <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>s</mi> <mi>h</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math> as ambient temperature dropped from 17.3 °C to −9.7 °C; (<b>d</b>) comparison of experimental and simulation values of <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>M</mi> <mi>H</mi> </mrow> </msub> </mrow> </semantics></math> as ambient temperature dropped from 17.3 °C to −9.7 °C.</p>
Full article ">Figure 13 Cont.
<p>Comparison of simulation results and experimental results for temperature variation curves of <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>s</mi> <mi>h</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>M</mi> <mi>H</mi> </mrow> </msub> </mrow> </semantics></math> under ambient temperature changes. (<b>a</b>) Comparison of experimental and simulation values of <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>s</mi> <mi>h</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math> as ambient temperature increased from −4.3 °C to 17.3 °C; (<b>b</b>) comparison of experimental and simulation values of <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>M</mi> <mi>H</mi> </mrow> </msub> </mrow> </semantics></math> as ambient temperature increased from −4.3 °C to 17.3 °C; (<b>c</b>) comparison of experimental and simulation values of <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>s</mi> <mi>h</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math> as ambient temperature dropped from 17.3 °C to −9.7 °C; (<b>d</b>) comparison of experimental and simulation values of <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>M</mi> <mi>H</mi> </mrow> </msub> </mrow> </semantics></math> as ambient temperature dropped from 17.3 °C to −9.7 °C.</p>
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19 pages, 7903 KiB  
Article
Fast Temperature Calculation Method for Spindle Servo Permanent Magnet Motors Under Full Operating Conditions Based on the Thermal Network Method
by Sheng Ma, Yijia Li, Xueyan Hao, Bo Zhang and Wei Feng
Electronics 2025, 14(4), 815; https://doi.org/10.3390/electronics14040815 - 19 Feb 2025
Abstract
In CNC machines, the temperature field analysis of spindle servo permanent magnet motors (SSPMMs) under rated load, overload, and weak magnetic conditions is critical for ensuring stable operation and machining accuracy. This paper proposes a temperature calculation method for SSPMMs based on the [...] Read more.
In CNC machines, the temperature field analysis of spindle servo permanent magnet motors (SSPMMs) under rated load, overload, and weak magnetic conditions is critical for ensuring stable operation and machining accuracy. This paper proposes a temperature calculation method for SSPMMs based on the thermal network method, which is used to quickly evaluate the temperature performance of SSPMMs under different operating conditions during design. This method can calculate the steady-state or transient temperature rise under different operating conditions. First, the electromagnetic performance and heat sources of the SSPMMs were analyzed. Then, based on the thermal network method, the equivalent thermal resistances and equivalent heat dissipation coefficients of the motor components were calculated. By iterating the heat balance equation or solving the heat conduction equation for different operating conditions, the temperature distribution of SSPMMs under different operating conditions was obtained. The accuracy of the thermal network model was validated through temperature analysis using fluid–structure interaction simulations and prototype testing. The results show that the relative error between the winding temperature calculated by the proposed equivalent thermal network model and the measured temperature under different operating conditions is less than 5%. This paper provides a theoretical basis for the thermal management of SSPMM, which can quickly and accurately evaluate the temperature rise in the motor during design. Full article
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Figure 1
<p>Structure of water-cooled 12-pole 54-slot SSPMM.</p>
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<p>Efficiency map of SSPMM.</p>
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<p>The thermal network structure of SSPMM.</p>
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<p>The sub-thermal network structure of SSPMM. (<b>a</b>) node 30; (<b>b</b>) node 8; (<b>c</b>) node 17.</p>
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<p>SSPMM temperature calculation flowchart based on thermal network method.</p>
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<p>Three-dimensional model of SSPMM. (<b>a</b>) Spiral waterway structure; (<b>b</b>) three-dimensional structure section view.</p>
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<p>Temperature distributions in SSPMM under rated condition: (<b>a</b>) stator; (<b>b</b>) stator winding; (<b>c</b>) permanent magnet; and (<b>d</b>) cooling water.</p>
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<p>Temperature distributions in SSPMM under high speed condition: (<b>a</b>) stator; (<b>b</b>) stator winding; (<b>c</b>) permanent magnet; and (<b>d</b>) cooling water.</p>
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<p>Prototype temperature test: (<b>a</b>) motor test system; (<b>b</b>) mechanical back-to-back test bench.</p>
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<p>Test temperature curves under rated conditions.</p>
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<p>Test temperature curves under overload condition.</p>
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15 pages, 3935 KiB  
Article
Study on the Vibration Characteristics of Separated Armature Assembly in an Electro-Hydraulic Servo Valve Under Interference Fit
by Tong Li, Jinghui Peng, Songjing Li, Juan Zhang and Aiying Zhang
Actuators 2025, 14(2), 98; https://doi.org/10.3390/act14020098 - 19 Feb 2025
Abstract
The electro-hydraulic servo valve is a critical component that transforms electrical signals into hydraulic signals, thereby controlling the hydraulic system. It finds extensive application in precision control systems. The stability of the electro-hydraulic servo valve is primarily influenced by the armature assembly. Unlike [...] Read more.
The electro-hydraulic servo valve is a critical component that transforms electrical signals into hydraulic signals, thereby controlling the hydraulic system. It finds extensive application in precision control systems. The stability of the electro-hydraulic servo valve is primarily influenced by the armature assembly. Unlike integral armature assembly, the separated armature assembly, comprising the armature, spring tube, flapper, and feedback spring, is joined through an interference fit, which introduces prestress within the assembly. The existence of prestress may affect the operational mode of the armature assembly. Consequently, this paper investigates the vibration characteristics of the separated armature assembly under interference fit conditions. Comparative analysis reveals that interference fit indeed generates prestress, which cannot be overlooked. To further validate the reliability of the simulation results, the natural frequency of the separated armature assembly is determined by applying a sweeping frequency signal to the torque motor using an electric drive, thereby verifying the feasibility of the simulation analysis. Additionally, the impact of interference on the vibration characteristics of the separated armature assembly is examined, confirming the accuracy of the simulation analysis method based on the interference fit. The research on vibration characteristics of a separated armature assembly provides technical support for the structural optimization design of the electro-hydraulic servo valve, thereby enhancing its performance. Full article
(This article belongs to the Special Issue Recent Developments in Precision Actuation Technologies)
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<p>Separated armature assembly.</p>
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<p>Meshing. (<b>a</b>) Node coupling is not considered in meshing; (<b>b</b>) node coupling is considered in meshing.</p>
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<p>Contact setting.</p>
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<p>Calculation results.</p>
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<p>Deformation nephogram.</p>
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<p>Stress nephogram.</p>
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<p>Modal analysis results under interference fit. (<b>a</b>) First-order vibration mode. (<b>b</b>) Second-order vibration mode. (<b>c</b>) Third-order vibration mode. (<b>d</b>) Fourth-order vibration mode. (<b>e</b>) Fifth-order vibration mode. (<b>f</b>) Sixth-order vibration mode.</p>
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<p>Modal test of armature assembly. (<b>a</b>) Schematic diagram of the experimental system. (<b>b</b>) Data acquisition system.</p>
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<p>Frequency domain analysis of armature end displacement under sweep signal.</p>
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<p>The influence of interference on the modes of armature assembly. (<b>a</b>) The influence of interference between the armature and the spring tube on the modes. (<b>b</b>) The influence of interference between the spring tube and the flapper on the modes. (<b>c</b>) The influence of interference between the flapper and the feedback spring on the modes.</p>
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<p>The influence of interference on the modes of armature assembly. (<b>a</b>) The influence of interference between the armature and the spring tube on the modes. (<b>b</b>) The influence of interference between the spring tube and the flapper on the modes. (<b>c</b>) The influence of interference between the flapper and the feedback spring on the modes.</p>
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18 pages, 3477 KiB  
Article
Optimization of Active Disturbance Rejection Control System for Vehicle Servo Platform Based on Artificial Intelligence Algorithm
by Fei Yang, Xiaopeng Su and Xuemei Ren
Electronics 2025, 14(4), 752; https://doi.org/10.3390/electronics14040752 - 14 Feb 2025
Abstract
The rapid growth of automotive intelligence and automation technology has made it difficult for traditional in-vehicle servo systems to satisfy the demands of modern intelligent systems when facing complex problems such as external disturbances, nonlinearity, and parameter uncertainty. To improve the anti-interference ability [...] Read more.
The rapid growth of automotive intelligence and automation technology has made it difficult for traditional in-vehicle servo systems to satisfy the demands of modern intelligent systems when facing complex problems such as external disturbances, nonlinearity, and parameter uncertainty. To improve the anti-interference ability and control accuracy of the system, this study proposes a joint control method of electronic mechanical braking control combined with the anti-lock braking system. This method has developed a new type of actuator in the electronic mechanical brake control system and introduced a particle swarm optimization algorithm to optimize the parameters of the self-disturbance rejection control system. At the same time, it combines an adaptive inversion algorithm to optimize the anti-lock braking system. The results indicated that the speed variation of the developed actuator and the actual signal completely stopped at 1.9 s. During speed control and deceleration, the actuator could respond quickly and accurately to control commands as expected. On an asphalt pavement, the maximum slip rate error of the optimized control method was 0.0428, while the original control method was 0.0492. The optimized method reduced the maximum error by about 12.9%. On icy and snowy roads, the maximum error of the optimization method was 0.0632, significantly lower than the original method’s 0.1266. The optimization method could significantly reduce slip rate fluctuations under extreme road conditions. The proposed method can significantly improve the control performance of the vehicle-mounted servo platform, reduce the sensitivity of the system to external disturbances, and has high practical value. Full article
(This article belongs to the Section Electrical and Autonomous Vehicles)
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<p>EMB system structure and working mechanism.</p>
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<p>Composition of EMB actuator model constructed.</p>
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<p>ADRC structure diagram.</p>
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<p>PSO algorithm participating in optimizing ADRC controller process.</p>
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<p>Joint control structure.</p>
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<p>Optimize the control framework of ABS system.</p>
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<p>Joint control model architecture.</p>
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<p>Performance comparison of EMB actuators.</p>
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<p>Clamping force comparison.</p>
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<p>Effect of the EMB multi-stage control approach.</p>
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<p>Asphalt pavement test results.</p>
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<p>Ice pavement test results.</p>
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21 pages, 6471 KiB  
Article
A Compact Low-Power Chopper Low Noise Amplifier for High Density Neural Front-Ends
by Alessandro Fava, Francesco Centurelli, Pietro Monsurrò and Giuseppe Scotti
Sensors 2025, 25(4), 1157; https://doi.org/10.3390/s25041157 - 13 Feb 2025
Abstract
This paper presents a low-power and area-efficient chopper-stabilized low noise amplifier (CS-LNA) for in-pixel neural recording systems. The proposed CS-LNA can be used in a multi-channel architecture, in which the chopper mixers of the LNA are exploited to provide the time division multiplexing [...] Read more.
This paper presents a low-power and area-efficient chopper-stabilized low noise amplifier (CS-LNA) for in-pixel neural recording systems. The proposed CS-LNA can be used in a multi-channel architecture, in which the chopper mixers of the LNA are exploited to provide the time division multiplexing (TDM) of several channels, while reducing the flicker noise and rejecting the Electrode DC Offset (EDO). A detailed noise analysis including the effect of the chopper stabilization on flicker noise, and a design flow to optimize the trade-off between input-referred noise and silicon area are presented, and utilized to design the LNA. The adopted approach to reject the EDO allows to tolerate an input offset of ±50 mV, without appreciably affecting the CS-LNA performance, and does not require an additional DC Servo Loop (DSL). The proposed CS-LNA has been fabricated in a 0.13 μm CMOS process with an area of 0.0268 mm2, consuming about 2 μA from a 0.8 V supply voltage. It achieves an integral noise of 4.19 μVrms (2.58 μVrms) from 1 to 7.5 kHz (from 300 to 7.5 kHz) and results in a noise efficiency factor (NEF) of 2.63 (1.62). Besides achieving a maximum gain of 38.67 dB with a tuning range of about 12 dB, the neural amplifier exhibits a CMRR of 67 dB. A comparison with the recent literature dealing with in-pixel amplifiers shows state-of-the-art performance. Full article
(This article belongs to the Section Biomedical Sensors)
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<p>Block scheme of the proposed high-density neural front-end exploiting chopper-based time division multiplexing (TDM).</p>
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<p>Block scheme with the electrode-electrolyte interface model.</p>
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<p>The proposed LNA architecture with input and output chopper mixers.</p>
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<p>OTA architecture: (<b>a</b>) schematic of OTA that uses auxiliary MOSFETs to steal current at output branch to maximize the output resistance and increase the voltage gain; (<b>b</b>) the common-mode feedback (CMFB) loop that exploits two pseudo-resistors as common mode estimator.</p>
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<p>Transistor-level implementation of chopper mixers.</p>
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<p>The signal processing of the chopper stabilization technique.</p>
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<p>Die microphotograph showing the detail of the in-pixel LNA prototype.</p>
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<p>Frequency response of the CS-LNA for different values of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> <mi>R</mi> </mrow> <mrow> <mi>o</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math> from PAC simulation.</p>
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<p>Histogram of CMRR for 200 mismatch Monte Carlo iterations.</p>
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<p>Histogram of PSRR for 200 mismatch Monte Carlo iterations.</p>
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<p>Histogram of output offset voltage for 200 mismatch Monte Carlo iterations.</p>
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<p>Photo of the measurement setup showing the test chip board and an FPGA board for clock generation, together with an arbitrary waveform generator and a digital oscilloscope.</p>
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<p>Measured LNA gain with programmable range from about 27 to 39 dB. (Chopping frequency set at 9 kHz. Minimum measured frequency is 1 Hz due to measurement setup limitations).</p>
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<p>Measured input noise PSD for chopping frequency set at 9 kHz (black trace) and at 15 kHz (red trace).</p>
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<p>Measured THD vs. frequency for an input amplitude of 1 mV.</p>
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<p>Measured THD vs. input amplitude at the frequency of 1 kHz.</p>
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15 pages, 4940 KiB  
Article
Research on Image Motion Compensation Technology in Vehicle-Mounted Photoelectric Servo System
by Mingyang Zhang, Yunjie Teng, Jingyi Fu and Tongyu Liu
Photonics 2025, 12(2), 154; https://doi.org/10.3390/photonics12020154 - 13 Feb 2025
Abstract
In order to improve the imaging quality of the vehicle photoelectric servo system, image motion compensation under the vehicle platform is studied. Based on the principle of image motion compensation, combined with coordinate system transformation and velocity vector decomposition, the angular velocity compensation [...] Read more.
In order to improve the imaging quality of the vehicle photoelectric servo system, image motion compensation under the vehicle platform is studied. Based on the principle of image motion compensation, combined with coordinate system transformation and velocity vector decomposition, the angular velocity compensation formula of a fast mirror in dynamic scanning imaging of a vehicle photoelectric servo system is obtained. A discrete sliding mode control algorithm based on the Kalman filter is proposed. The proposed algorithm and the discrete sliding mode control algorithm are simulated and compared to verify the system control performance. The simulation results show that the designed algorithm improves control accuracy by 76.3%, reduces overshoot by 75%, and improves response time by 31.25% compared with the discrete sliding mode control algorithm. The experimental platform is built to verify the experimental results. The experimental results show that the speed stability accuracy of the fast mirror is better than 19 μrad, which is 74.37% higher than that of the traditional control scheme. This study provides a reference for the follow-up study of image motion compensation in a vehicle photoelectric servo system. Full article
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<p>Vehicle photoelectric servo system imaging model.</p>
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<p>Velocity vector decomposition diagram.</p>
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<p>Coordinate system transformation diagram.</p>
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<p>Servo system control block diagram.</p>
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<p>KF-DSMC control block diagram.</p>
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<p>Step response signal diagram.</p>
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<p>Vibration spectrum of a car traveling at 10 m/s.</p>
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<p>Noise signal diagrams. (<b>a</b>) Process noise signal diagram. (<b>b</b>) Random noise signal diagram.</p>
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<p>Sinusoidal signal tracking diagram.</p>
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<p>Stabilization accuracy error plot for sinusoidal signals.</p>
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<p>Field experiment diagram. (<b>a</b>) Image motion compensation device diagram and vehicle photoelectric servo system diagram. (<b>b</b>) Image motion compensation device diagram.</p>
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<p>Angular velocity curve of image motion compensation.</p>
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<p>Stabilization accuracy error of fast steering mirror.</p>
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<p>Image motion compensation comparison diagrams. (<b>a</b>) Image without image motion compensation. (<b>b</b>) Image after image motion compensation.</p>
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25 pages, 4475 KiB  
Article
Servo Collision Detection Control System Based on Robot Dynamics
by Qinjian Xiang, Chao Chen and Yadong Jiang
Sensors 2025, 25(4), 1131; https://doi.org/10.3390/s25041131 - 13 Feb 2025
Abstract
Collision detection and inspection of industrial robots have become essential functions in modern industrial automation. Sensor-based detection methods are commonly employed in research to achieve collision detection, including high-precision force sensors, ultrasonic ranging sensors, electronic skins, and others. While collision detection using force [...] Read more.
Collision detection and inspection of industrial robots have become essential functions in modern industrial automation. Sensor-based detection methods are commonly employed in research to achieve collision detection, including high-precision force sensors, ultrasonic ranging sensors, electronic skins, and others. While collision detection using force sensors or electronic skin sensors offers very high accuracy, the inclusion of these sensors increases the overall cost. This article proposes a solution using dynamic modeling for collision detection. First, the theoretical torque generated by each axis of the industrial robot under different pose conditions is analyzed in real time. Then, the actual torque is calculated by sampling the motor current of each axis. By setting error margins and collision detection thresholds, collision detection can be achieved in a cost-effective manner without the need for additional sensors. Experiments were conducted to evaluate this dynamic modeling approach to collision detection. The findings indicated that the approach is efficacious and capable of identifying the impacts of diverse collision objects. However, compared to sensor-based detection methods, collision detection using dynamic modeling has the disadvantage of lower accuracy. Future research will concentrate on enhancing the calculation accuracy of the theoretical torque to enhance the sensitivity of collision detection. Full article
(This article belongs to the Section Sensors and Robotics)
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<p>Topology diagram of servo hardware.</p>
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<p>Software design flowchart.</p>
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<p>Servo control framework.</p>
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<p>The calculation process of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>I</mi> </mrow> <mrow> <mi>q</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>(<b>a</b>) Industrial robots are used in actual testing. (<b>b</b>) A six-axis coordinate system based on actual industrial robots.</p>
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<p>The waveform of calculated torque and feedback torque of each joint during normal operation of the robot. (<b>a</b>) Joint 1 calculates the torque and feedback torque; (<b>b</b>) Joint 2 calculates the torque and feedback torque; (<b>c</b>) Joint 3 calculates the torque and feedback torque; (<b>d</b>) Joint 4 calculates the torque and feedback torque; (<b>e</b>) Joint 5 calculates the torque and feedback torque; (<b>f</b>) Joint 6 calculates the torque and feedback torque.</p>
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<p>Robots collide while running along the <span class="html-italic">X</span>-axis direction.</p>
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<p>Calculation of torque and feedback torque waveform before and after robot collision in the <span class="html-italic">X</span>-axis direction (The red line represents the calculated torque, and the blue line represents the feedback torque). (<b>a</b>) When the robot runs along the <span class="html-italic">X</span>-axis, the calculated torque and feedback torque of joint 2. (<b>b</b>) Calculation of torque and feedback torque of joint 2 before and after the collision. (<b>c</b>) When a collision occurs, the calculated torque and feedback torque of joint 2. (<b>d</b>) When the robot runs along the <span class="html-italic">X</span>-axis, the calculated torque and feedback torque of joint 3. (<b>e</b>) Calculation of torque and feedback torque of joint 3 before and after the collision. (<b>f</b>) When a collision occurs, the calculated torque and feedback torque of joint 3. (<b>g</b>) When the robot runs along the <span class="html-italic">X</span>-axis, the calculated torque and feedback torque of joint 5. (<b>h</b>) Calculation of torque and feedback torque of joint 5 before and after the collision. (<b>i</b>) When a collision occurs, the calculated torque and feedback torque of joint 5.</p>
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<p>Robots collide while running along the <span class="html-italic">Y</span>-axis direction.</p>
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<p>Calculation of torque and feedback torque waveform before and after robot collision in the <span class="html-italic">Y</span>-axis direction (The red line represents the calculated torque, and the blue line represents the feedback torque). (<b>a</b>) When the robot runs along the <span class="html-italic">Y</span>-axis, the calculated torque and feedback torque of joint 1. (<b>b</b>) Calculation of torque and feedback torque of joint 1 before and after the collision. (<b>c</b>) When a collision occurs, the calculated torque and feedback torque of joint 1. (<b>d</b>) When the robot runs along the <span class="html-italic">Y</span>-axis, the calculated torque and feedback torque of joint 2. (<b>e</b>) Calculation of torque and feedback torque of joint 2 before and after the collision. (<b>f</b>) When a collision occurs, the calculated torque and feedback torque of joint 2. (<b>g</b>) When the robot runs along the <span class="html-italic">Y</span>-axis, the calculated torque and feedback torque of joint 3. (<b>h</b>) Calculation of torque and feedback torque of joint 3 before and after the collision. (<b>i</b>) When a collision occurs, the calculated torque and feedback torque of joint 3. (<b>j</b>) When the robot runs along the <span class="html-italic">Y</span>-axis, the calculated torque and feedback torque of joint 5. (<b>k</b>) Calculation of torque and feedback torque of joint 5 before and after the collision. (<b>l</b>) When a collision occurs, the calculated torque and feedback torque of joint 5. (<b>m</b>) When the robot runs along the <span class="html-italic">Y</span>-axis, the calculated torque and feedback torque of joint 6. (<b>n</b>) Calculation of torque and feedback torque of joint 6 before and after collision. (<b>o</b>) When a collision occurs, the calculated torque and feedback torque of joint 6.</p>
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<p>Calculation of torque and feedback torque waveform before and after robot collision in the <span class="html-italic">Y</span>-axis direction (The red line represents the calculated torque, and the blue line represents the feedback torque). (<b>a</b>) When the robot runs along the <span class="html-italic">Y</span>-axis, the calculated torque and feedback torque of joint 1. (<b>b</b>) Calculation of torque and feedback torque of joint 1 before and after the collision. (<b>c</b>) When a collision occurs, the calculated torque and feedback torque of joint 1. (<b>d</b>) When the robot runs along the <span class="html-italic">Y</span>-axis, the calculated torque and feedback torque of joint 2. (<b>e</b>) Calculation of torque and feedback torque of joint 2 before and after the collision. (<b>f</b>) When a collision occurs, the calculated torque and feedback torque of joint 2. (<b>g</b>) When the robot runs along the <span class="html-italic">Y</span>-axis, the calculated torque and feedback torque of joint 3. (<b>h</b>) Calculation of torque and feedback torque of joint 3 before and after the collision. (<b>i</b>) When a collision occurs, the calculated torque and feedback torque of joint 3. (<b>j</b>) When the robot runs along the <span class="html-italic">Y</span>-axis, the calculated torque and feedback torque of joint 5. (<b>k</b>) Calculation of torque and feedback torque of joint 5 before and after the collision. (<b>l</b>) When a collision occurs, the calculated torque and feedback torque of joint 5. (<b>m</b>) When the robot runs along the <span class="html-italic">Y</span>-axis, the calculated torque and feedback torque of joint 6. (<b>n</b>) Calculation of torque and feedback torque of joint 6 before and after collision. (<b>o</b>) When a collision occurs, the calculated torque and feedback torque of joint 6.</p>
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<p>Robots collide while running along the <span class="html-italic">Z</span>-axis direction.</p>
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<p>Calculation of torque and feedback torque waveform before and after robot collision in the <span class="html-italic">Z</span>-axis direction (The red line represents the calculated torque, and the blue line represents the feedback torque). (<b>a</b>) When the robot runs along the <span class="html-italic">Z</span>-axis, the calculated torque and feedback torque of joint 2. (<b>b</b>) Calculation of torque and feedback torque of joint 2 before and after the collision. (<b>c</b>) When a collision occurs, the calculated torque and feedback torque of joint 2. (<b>d</b>) When the robot runs along the <span class="html-italic">Z</span>-axis, the calculated torque and feedback torque of joint 3. (<b>e</b>) Calculation of torque and feedback torque of joint 3 before and after the collision. (<b>f</b>) When a collision occurs, the calculated torque and feedback torque of joint 3. (<b>g</b>) When the robot runs along the <span class="html-italic">Z</span>-axis, the calculated torque and feedback torque of joint 5. (<b>h</b>) Calculation of torque and feedback torque of joint 5 before and after the collision. (<b>i</b>) When a collision occurs, the calculated torque and feedback torque of joint 5.</p>
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<p>Calculation of torque and feedback torque waveform before and after robot collision in the <span class="html-italic">Z</span>-axis direction (The red line represents the calculated torque, and the blue line represents the feedback torque). (<b>a</b>) When the robot runs along the <span class="html-italic">Z</span>-axis, the calculated torque and feedback torque of joint 2. (<b>b</b>) Calculation of torque and feedback torque of joint 2 before and after the collision. (<b>c</b>) When a collision occurs, the calculated torque and feedback torque of joint 2. (<b>d</b>) When the robot runs along the <span class="html-italic">Z</span>-axis, the calculated torque and feedback torque of joint 3. (<b>e</b>) Calculation of torque and feedback torque of joint 3 before and after the collision. (<b>f</b>) When a collision occurs, the calculated torque and feedback torque of joint 3. (<b>g</b>) When the robot runs along the <span class="html-italic">Z</span>-axis, the calculated torque and feedback torque of joint 5. (<b>h</b>) Calculation of torque and feedback torque of joint 5 before and after the collision. (<b>i</b>) When a collision occurs, the calculated torque and feedback torque of joint 5.</p>
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21 pages, 3397 KiB  
Article
A Novel Filtering Observer: A Cost-Effective Estimation Solution for Industrial PMSM Drives Using in-Motion Control Systems
by Cagatay Dursun and Selin Ozcira Ozkilic
Energies 2025, 18(4), 883; https://doi.org/10.3390/en18040883 - 12 Feb 2025
Abstract
This paper presents a cost-efficient estimation method, the filtering observer (FOBS), which provides a smooth estimation through prior estimation, enhancing the field-oriented control (FOC) performance of motion control systems by estimating the angular rotor position, angular rotor velocity, and disturbance torque of permanent [...] Read more.
This paper presents a cost-efficient estimation method, the filtering observer (FOBS), which provides a smooth estimation through prior estimation, enhancing the field-oriented control (FOC) performance of motion control systems by estimating the angular rotor position, angular rotor velocity, and disturbance torque of permanent magnet synchronous motors (PMSMs). The cost-effective FOBS demonstrates characteristics akin to optimal estimating methods and employs arbitrary pole placement, facilitating more straightforward adjustment of the FOBS gain. The non-linear characteristics of low-resolution and low-cost encoders, the computation of angular rotor velocity using traditional techniques, and disturbances over broad frequency ranges in the servo drive system impair the efficacy of the motion control system. As a cost-effective solution, the FOBS minimizes the deficiencies of the low-cost encoder, reduces oscillations and measurement delays in the speed feedback signal, and provides smooth estimation of disturbance torque. Based on the results from experiments, the FOBS was compared against traditional approaches and the performance of the motion control system was examined. Also, the performance of the motion control system was investigated. The results indicate that these enhancements were achieved with low processing power and an easily implementable estimate technique suitable for low-cost industrial systems. Full article
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<p>Illustration of M/T-method speed estimation method.</p>
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<p>Timeline of a priori and a posteriori state estimation.</p>
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<p>Illustration of proposed scheme for industrial PMSM drive system.</p>
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<p>(<b>a</b>) Pole and zero locations of the designed FOBS. (<b>b</b>) Variation of pole and zero locations for different inertias. (<b>c</b>) Variation of pole and zero locations for different sampling times. (<b>d</b>) Zoomed view of variation of pole and zero locations for different sampling times.</p>
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<p>(<b>a</b>) Block diagram of drive system. (<b>b</b>) Illustration of experimental test set-up.</p>
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<p>(<b>a</b>) Comparison of position feedback measured by QEP and FOBS at 600 r/min. (<b>b</b>) Zoomed response of position feedback measured by QEP and FOBS at 600 r/min. (<b>c</b>) Position error between QEP and FOBS at 30 r/min. (<b>d</b>) Comparison of position feedback measured by QEP and FOBS at 30 r/min. (<b>e</b>) Zoomed response of position feedback measured by QEP and FOBS at 30 r/min. (<b>f</b>) Position error between QEP and FOBS at 30 r/min.</p>
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<p>Velocity estimation performances of mentioned estimation methods: (<b>a</b>) 30 r/min, (<b>b</b>) zoomed view at 30 r/min, (<b>c</b>) zoomed view at 30 r/min with the FOBS and SSKF, (<b>d</b>) 600 r/min, (<b>e</b>) zoomed view at 600 r/min, (<b>f</b>) zoomed view at 600 r/min with the FOBS and SSKF, (<b>g</b>) 1800 r/min, (<b>h</b>) zoomed view at 1800 r/min, (<b>i</b>) zoom edview at 1800 r/min with the FOBS and SSKF.</p>
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<p>(<b>a</b>) Estimation of <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>T</mi> </mrow> <mo>^</mo> </mover> </mrow> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mi>k</mi> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math> by the FOBS, SSKF, and SOBS under 0.5 Nm steady load. (<b>b</b>) Estimation of <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>T</mi> </mrow> <mo>^</mo> </mover> </mrow> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mi>k</mi> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math> by the FOBS, SSKF, and SOBS under 1 Nm steady load.</p>
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<p>(<b>a</b>) Position loop response of the control system and estimation of angular position under 1 Nm steady load with various inertia mismatches. (<b>b</b>) Speed loop response of the control system and estimation of angular velocity under 1 Nm steady load with various inertia mismatches. (<b>c</b>) Disturbance torque estimation under 1 Nm steady load with various inertia mismatches.</p>
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<p>Execution times of mentioned estimation methods measured by a logic analyzer. (<b>a</b>) M/T method at moderate speed. (<b>b</b>) M/T method at low speed. (<b>c</b>) SOBS. (<b>d</b>) FOBS. (<b>e</b>) SSKF.</p>
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25 pages, 12900 KiB  
Article
Coupling Effect of Waves and Currents on Dynamic Responses of a Semi-Submerged Floating Wind Turbine
by Bang Wu, Biswajit Basu, Lin Chen, Xugang Hua and Wenxi Wang
Appl. Sci. 2025, 15(4), 1802; https://doi.org/10.3390/app15041802 - 10 Feb 2025
Abstract
The effects of wave and current on floating offshore wind turbines (FOWTs) are usually treated separately without considering their inherent interaction. In this study, the coupling effect of wave and current on the dynamic responses of a semi-submerged FOWT carrying a 5-MW NREL [...] Read more.
The effects of wave and current on floating offshore wind turbines (FOWTs) are usually treated separately without considering their inherent interaction. In this study, the coupling effect of wave and current on the dynamic responses of a semi-submerged FOWT carrying a 5-MW NREL turbine is investigated. A numerical model considering the wave–current interaction is introduced, which accounts for the frequency shifts and surface profile changes for waves traveling over currents. The dynamic structural model of the semi-submerged FOWT is established in ANSYS AQWA, where the aero-servo-structural loadings on tower and turbine were obtained from the FAST platform by using the FAST-to-AQWA coding program. Irregular waves with 1- and 50-year return periods, in conjunction with a uniform current, were adopted to evaluate the coupling interaction effects. Waves traveling on positive and on opposite currents are examined in different cases with waves and currents propagating along the surge or sway direction. Waves consistently propagate along positive surge or sway direction. Waves interacting with positive or opposite currents have dramatically different modifications on the wave spectrum. Differences of up to 22% are recorded by comparing both the main motions and mooring tension when the interaction of waves and currents is considered or not. The coupling interaction between waves and currents has a limited influence on the tower base shear forces and bending moments. It was found that a straightforward superposition approach to evaluate the effect of the waves and the currents may underestimate the dynamic motions and mooring tension of FOWTs. Full article
(This article belongs to the Special Issue Advances in Structural Vibration Control)
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<p>The schematic diagram of the CSS FOWT with three mooring cables.</p>
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<p>The flowchart of the entire simulation process in F2A.</p>
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<p>Definition of coordinate system of interaction models for wave and current.</p>
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<p>Turbulent wind field with a reference wind speed of 12 m/s at the hub height.</p>
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<p>Current effects on the PM spectrum of 50-year return period.</p>
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<p>Time histories of the main motions for case 5 in the X direction.</p>
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<p>Time histories of the main motions for case 5 in the X direction.</p>
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<p>Time histories of the main motions for case 5 in the Y direction.</p>
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<p>The mean and standard deviation values of the main motions in the X direction.</p>
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<p>The mean and standard deviation values of the main motions in the X direction.</p>
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<p>The mean and standard deviation values of the main motions in the Y direction.</p>
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<p>Dynamic response of surge motion for case 7 in the X direction.</p>
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<p>Dynamic response of sway motion for case 8 in the Y direction.</p>
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<p>Dynamic response of sway motion for case 8 in the Y direction.</p>
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<p>The mean and standard deviation values of the mooring tension in the X direction.</p>
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<p>The mean and standard deviation values of the mooring tension in the Y direction.</p>
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<p>The mean and standard deviation values of the mooring tension in the Y direction.</p>
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<p>Maximum mooring tensions of various cables when the waves and currents are in the Y direction.</p>
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<p>Dynamic response of the mooring tension of the CSS FOWT for case 8 in the Y direction.</p>
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<p>The mean and standard deviation values of the tower base shear force and bending moment in the X direction.</p>
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<p>The mean and standard deviation values of the tower base shear force and bending moment in the Y direction.</p>
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<p>The mean and standard deviation values of the tower base shear force and bending moment in the Y direction.</p>
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<p>PSD of the tower base forces for case 8 in the X direction.</p>
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<p>PSD of the tower base forces for case 8 in the X direction.</p>
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<p>PSD of the tower base forces for case 8 in the Y direction.</p>
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26 pages, 12201 KiB  
Article
MPG-YOLO: Enoki Mushroom Precision Grasping with Segmentation and Pulse Mapping
by Limin Xie, Jun Jing, Haoyu Wu, Qinguan Kang, Yiwei Zhao and Dapeng Ye
Agronomy 2025, 15(2), 432; https://doi.org/10.3390/agronomy15020432 - 10 Feb 2025
Abstract
The flatness of the cut surface in enoki mushrooms (Flammulina filiformis Z.W. Ge, X.B. Liu & Zhu L. Yang) is a key factor in quality classification. However, conventional automatic cutting equipment struggles with deformation issues due to its inability to adjust the [...] Read more.
The flatness of the cut surface in enoki mushrooms (Flammulina filiformis Z.W. Ge, X.B. Liu & Zhu L. Yang) is a key factor in quality classification. However, conventional automatic cutting equipment struggles with deformation issues due to its inability to adjust the grasping force based on individual mushroom sizes. To address this, we propose an improved method that integrates visual feedback to dynamically adjust the execution end, enhancing cut precision. Our approach enhances YOLOv8n-seg with Star Net, SPPECAN (a reconstructed SPPF with efficient channel attention), and C2fDStar (C2f with Star Net and deformable convolution) to improve feature extraction while reducing computational complexity and feature loss. Additionally, we introduce a mask ownership judgment and merging optimization algorithm to correct positional offsets, internal disconnections, and boundary instabilities in grasping area predictions. Based on this, we optimize grasping parameters using an improved centroid-based region width measurement and establish a region width-to-PWM mapping model for the precise conversion from visual data to gripper control. Experiments in real-situation settings demonstrate the effectiveness of our method, achieving a mean average precision (mAP50:95) of 0.743 for grasping area segmentation, a 4.5% improvement over YOLOv8, with an average detection speed of 10.3 ms and a target width measurement error of only 0.14%. The proposed mapping relationship enables adaptive end-effector control, resulting in a 96% grasping success rate and a 98% qualified cutting surface rate. These results confirm the feasibility of our approach and provide a strong technical foundation for the intelligent automation of enoki mushroom cutting systems. Full article
(This article belongs to the Section Soil and Plant Nutrition)
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<p>Study flow of intelligent gripping method for bottle-planted enoki mushrooms.</p>
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<p>Introduction to enoki mushroom samples. The star represents the center point of the data.</p>
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<p>Region of interest (ROI) error prediction graph result. (<b>a</b>) mask positions were shifted; (<b>b</b>) masks incorrectly covered.</p>
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<p>Label information analysis and statistics.</p>
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<p>StarNet structure.</p>
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<p>SPPECAN structure.</p>
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<p>C2fDStar structure.</p>
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<p>Parent–child relationship judgment algorithm. ‘x’ stands for existential problem.</p>
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<p>Mask-merging algorithm.</p>
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<p>How weighted box fusion (WBF) and box fusion work.</p>
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<p>Schematic diagram of the optimization algorithm.</p>
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<p>Communication, mapping, and control flowchart.</p>
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<p>Compression experimental curves of three regions of bottle-planted enoki mushrooms. (<b>a</b>) Force curves of Region A under different compression conditions. (<b>b</b>) Force curves of Region B under different compression conditions. (<b>c</b>) Force curves of Region C under different compression conditions. (<b>d</b>) Fitting of multiple load-displacement curves in three regions.</p>
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<p>Training/validation loss curve.</p>
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<p>Visualization of results before and after model ensemble.</p>
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<p>Comparison of recognition performance between traditional algorithms and MPG-YOLOv8. (<b>a</b>) Results 1 (MPG-YOLO v8); (<b>b</b>) Results 1 (Conventional method); (<b>c</b>) Results 2 (MPG-YOLO v8); (<b>d</b>) Results 2 (Conventional method).</p>
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<p>Comparison of local mask optimization visualization. The circles and lines in the figure indicate the internal disconnection and boundary of the mask.</p>
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<p>Coordinate mapping experiment. (<b>a</b>) Fitting results before data enhancement; (<b>b</b>) Fitting results after data enhancement; (<b>c</b>) L data distribution; (<b>d</b>) LAB data distribution; The star represents the center point of the data.</p>
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<p>Coordinate mapping experiment. (<b>a</b>) Fitting results before data enhancement; (<b>b</b>) Fitting results after data enhancement; (<b>c</b>) L data distribution; (<b>d</b>) LAB data distribution; The star represents the center point of the data.</p>
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<p>Grabbing validity experiment. (<b>a</b>) MPG grab. (<b>b</b>) Reality grab. (<b>c</b>) Mapping box plot of PWM.</p>
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<p>Cutting plane contrast.</p>
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21 pages, 7934 KiB  
Article
Research on a New Method of Macro–Micro Platform Linkage Processing for Large-Format Laser Precision Machining
by Longjie Xiong, Haifeng Ma, Zheng Sun, Xintian Wang, Yukui Cai, Qinghua Song and Zhanqiang Liu
Micromachines 2025, 16(2), 177; https://doi.org/10.3390/mi16020177 - 31 Jan 2025
Abstract
In recent years, the macro–micro structure (servo platform for macro motion and galvanometer for micro motion) composed of a galvanometer and servo platform has been gradually applied to laser processing in order to address the increasing demand for high-speed, high-precision, and large-format precision [...] Read more.
In recent years, the macro–micro structure (servo platform for macro motion and galvanometer for micro motion) composed of a galvanometer and servo platform has been gradually applied to laser processing in order to address the increasing demand for high-speed, high-precision, and large-format precision machining. The research in this field has evolved from step-and-scan methods to linkage processing methods. Nevertheless, the existing linkage processing methods cannot make full use of the field-of-view (FOV) of the galvanometer. In terms of motion distribution, the existing methods are not suitable for continuous micro segments and generate the problem that the distribution parameter can only be obtained through experience or multiple experiments. In this research, a new laser linkage processing method for global trajectory smoothing of densely discretized paths is proposed. The proposed method can generate a smooth trajectory of the servo platform with bounded acceleration by the finite impulse response (FIR) filter under the global blending error constrained by the galvanometer FOV. Moreover, the trajectory of the galvanometer is generated by vector subtraction, and the motion distribution of macro–micro structure is accurately realized. Experimental verification is carried out on an experimental platform composed of a three-axis servo platform, a galvanometer, and a laser. Simulation experiment results indicate that the processing efficiency of the proposed method is improved by 79% compared with the servo platform processing only and 55% compared with the previous linkage processing method. Furthermore, the method can be successfully utilized on experimental platforms with good tracking performance. In summary, the proposed method adeptly balances efficiency and quality, rendering it particularly suitable for laser precision machining applications. Full article
(This article belongs to the Section E:Engineering and Technology)
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<p>The schematic diagram of laser linkage processing method.</p>
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<p>The flowchart of proposed laser linkage processing method.</p>
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<p>Velocity curve based on FIR filtering.</p>
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<p>Velocity and acceleration curve of twice filtering. (<b>a</b>) Long speed pulse (<span class="html-italic">T<sub>v</sub></span><sub>,<span class="html-italic">k</span></sub> &gt; <span class="html-italic">T</span><sub>1</sub> + <span class="html-italic">T</span><sub>2</sub>). (<b>b</b>) Short speed pulse (<span class="html-italic">T<sub>v</sub></span><sub>,<span class="html-italic">k</span></sub> = <span class="html-italic">T</span><sub>1</sub> + <span class="html-italic">T</span><sub>2</sub>) (<b>c</b>) Short speed pulse (<span class="html-italic">T<sub>v</sub></span><sub>,<span class="html-italic">k</span></sub> &lt; <span class="html-italic">T</span><sub>1</sub> &lt; <span class="html-italic">T</span><sub>2</sub>).</p>
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<p>Motion overlap of multiple micro segments.</p>
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<p>Contour error of two adjacent micro segments. (<b>a</b>) Initial G-code. (<b>b</b>) Target trajectory of adjacent micro segments. (<b>c</b>) Filtered velocity blending schematic diagram.</p>
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<p>Global blending error of multiple micro segments.</p>
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<p>Equal scale mapping.</p>
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<p>Simulation test and comparison. (<b>a</b>) Target pattern. (<b>b</b>–<b>d</b>) Trajectory distribution of SPO, PLP and proposed, respectively.</p>
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<p>Simulation results for butterfly-shaped trajectory. (<b>a1</b>–<b>a3</b>) Velocity, acceleration, and jerk of servo platform with SPO. (<b>b1</b>–<b>b3</b>) Velocity, acceleration, and jerk of servo platform with PLP. (<b>c1</b>–<b>c3</b>) Velocity, acceleration, and jerk of galvanometer with PLP. (<b>d1</b>–<b>d3</b>) Velocity, acceleration, and jerk of servo platform with the proposed. (<b>e1</b>–<b>e3</b>) Velocity, acceleration, and jerk of galvanometer with the proposed. (<b>f</b>) Servo platform velocity along X-axis of SOP, PLP, and the proposed, respectively.</p>
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<p>Comparison of PLP and the proposed. (<b>a</b>) Servo platform trajectory of PLP and the proposed. (<b>b</b>,<b>c</b>) Position of galvanometer’s axes of PLP and the proposed, respectively.</p>
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<p>Configuration of the experimental physical platform setup.</p>
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<p>Processing result diagram of the butterfly pattern and locally magnified diagram at the corner.</p>
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<p>Comparison between feedback velocity and commanded velocity: (<b>a</b>) Velocity of X-axis; (<b>b</b>) Velocity of Y-axis.</p>
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20 pages, 13817 KiB  
Article
Prescribed Performance Global Non-Singular Fast Terminal Sliding Mode Control of PMSM Based on Linear Extended State Observer
by Yifei Yao, Yaoming Zhuang, Yizhi Xie, Peng Xu and Chengdong Wu
Actuators 2025, 14(2), 65; https://doi.org/10.3390/act14020065 - 30 Jan 2025
Abstract
In manufacturing, the position tracking accuracy and stability of Permanent Magnet Synchronous Motors are often challenged by uncertainties, especially in complex environments. Existing control methods struggle to balance fast response with high-precision tracking. To address this, we propose a Prescribed Performance Global Non-Singular [...] Read more.
In manufacturing, the position tracking accuracy and stability of Permanent Magnet Synchronous Motors are often challenged by uncertainties, especially in complex environments. Existing control methods struggle to balance fast response with high-precision tracking. To address this, we propose a Prescribed Performance Global Non-Singular Fast Terminal Sliding Mode Control (PPGNFTSMC) method using a linear extended state observer (LESO). A smooth and bounded prescribed performance function is designed to ensure finite-time convergence while satisfying performance requirements such as overshoot and settling time. Based on this function, the system error is reconstructed to align the system response with predefined specifications. The reconstructed error is then used to design a global non-singular fast terminal sliding mode surface. A LESO is employed for real-time disturbance estimation, and the disturbance estimates, along with the sliding mode surface, are used to derive the control law for the position–speed integrated controller. Experimental results show that the proposed method outperforms the comparison methods in transient response, tracking accuracy, and robustness across various signal types. Full article
(This article belongs to the Special Issue New Control Schemes for Actuators—2nd Edition)
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<p>Structural diagram of PMSM.</p>
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<p>Overall block diagram of the system.</p>
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<p>System performance curves of simulations. (<b>a</b>) Position tracking curves. (<b>b</b>) Position tracking curves. (<b>c</b>) Position tracking error curves. (<b>d</b>) Position tracking error curves. (<b>e</b>) Control input curves. (<b>f</b>) Control input curves.</p>
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<p>Experiment platform.</p>
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<p>System performance curves of Experiment 1. (<b>a</b>) Position tracking curves. (<b>b</b>) Position tracking error curves. (<b>c</b>) Control input curves. (<b>d</b>) Disturbance estimation curves.</p>
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<p>System performance curves of Experiment 2. (<b>a</b>) Position tracking curves. (<b>b</b>) Position tracking error curves. (<b>c</b>) Control input curves. (<b>d</b>) Disturbance estimation curves.</p>
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<p>System performance curves of Experiment 3. (<b>a</b>) Position tracking curves. (<b>b</b>) Position tracking error curves. (<b>c</b>) Control input curves. (<b>d</b>) Disturbance estimation curves.</p>
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<p>System performance curves of Experiment 4. (<b>a</b>) Position tracking curves. (<b>b</b>) Position tracking error curves. (<b>c</b>) Control input curves. (<b>d</b>) Disturbance estimation curves.</p>
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<p>Effect of moment of inertia on experimental results. (<b>a</b>) Position tracking curves. (<b>b</b>) Position tracking error curves. (<b>c</b>) Control input curves.</p>
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16 pages, 6685 KiB  
Article
A Class of Anti-Windup Controllers for Precise Positioning of an X-Y Platform with Input Saturations
by Chung-Wei Chen, Hsiu-Ming Wu and Chau-Yih Nian
Electronics 2025, 14(3), 539; https://doi.org/10.3390/electronics14030539 - 28 Jan 2025
Abstract
The windup phenomenon occurs and results in performance degradation while the designed positioning controller output makes actuators saturated. This study presents significant and effective anti-windup controllers for performance improvement and comparison of the position tracking. To address real-world industrial scenarios, the trajectory with [...] Read more.
The windup phenomenon occurs and results in performance degradation while the designed positioning controller output makes actuators saturated. This study presents significant and effective anti-windup controllers for performance improvement and comparison of the position tracking. To address real-world industrial scenarios, the trajectory with a T-curve velocity profile is planned to regulate hardware limitations and maintain efficiency throughout the control process. At first, the dynamic model of an inertia load for a servo control system is established using Newton’s law of motion. Then, anti-windup controllers are designed and implemented based on basic PID controllers. The conducted simulations validate its effectiveness and feasibility. Finally, experimental results demonstrate that the proposed algorithms achieve smaller overshoot and faster settling time under input saturations when executing specific paths on the X-Y platform, even though the given control commands change. It is verified that the proposed approaches can, indeed, effectively mitigate the windup phenomenon, leading to improved positioning accuracy in industrial applications. Full article
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<p>Illustration of a typical servo system.</p>
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<p>Block diagram of a basic PID controller with the back-calculation.</p>
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<p>The simulation results using the back-calculation algorithm.</p>
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<p>Block diagram of basic PID controller with the clamping.</p>
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<p>The simulation results under different anti-windup algorithms.</p>
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<p>The control input responses in the simulation under different anti-windup algorithms.</p>
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<p>The experimental X-Y platform based on the servo motor system.</p>
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<p>(<b>a</b>) The Bézier curve designed for the experiment; (<b>b</b>,<b>c</b>) are, respectively, the independent motion commands for both servo motors.</p>
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<p>(<b>a</b>) The quadrilateral curve designed for the experiment; (<b>b</b>) and (<b>c</b>) are the independent motion commands for both servo motors, respectively.</p>
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<p>Experimental results for the Bézier curve: (<b>a</b>) without anti-windup, (<b>b</b>) with back-calculation algorithm, and (<b>c</b>) with clamping algorithm.</p>
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<p>Experimental results for the quadrilateral curve: (<b>a</b>) without anti-windup, (<b>b</b>) with back-calculation algorithm, and (<b>c</b>) with clamping algorithm.</p>
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<p>The tracking error responses under different cases (<b>a</b>) <span class="html-italic">X</span>–axis (<b>b</b>) <span class="html-italic">Y</span>–axis in Bézier curve.</p>
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<p>The tracking error responses under different cases (<b>a</b>) <span class="html-italic">X</span>–axis (<b>b</b>) <span class="html-italic">Y</span>–axis in quadrilateral curve.</p>
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<p>The velocity responses under different cases (<b>a</b>) <span class="html-italic">X</span>–axis (<b>b</b>) <span class="html-italic">Y</span>–axis in Bézier curve.</p>
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<p>The velocity responses under different cases (<b>a</b>) <span class="html-italic">X</span>–axis (<b>b</b>) <span class="html-italic">Y</span>–axis in quadrilateral curve.</p>
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<p>The control input responses under different cases (<b>a</b>) <span class="html-italic">X</span>–axis (<b>b</b>) <span class="html-italic">Y</span>–axis in Bézier curve.</p>
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<p>The control input responses under different cases (<b>a</b>) <span class="html-italic">X</span>–axis (<b>b</b>) <span class="html-italic">Y</span>–axis in quadrilateral curve.</p>
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