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Search Results (1,137)

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23 pages, 5855 KiB  
Article
A Novel AVR System Utilizing Fuzzy PIDF Enriched by FOPD Controller Optimized via PSO and Sand Cat Swarm Optimization Algorithms
by Mokhtar Shouran, Mohammed Alenezi, Mohamed Naji Muftah, Abdalmajid Almarimi, Abdalghani Abdallah and Jabir Massoud
Energies 2025, 18(6), 1337; https://doi.org/10.3390/en18061337 (registering DOI) - 8 Mar 2025
Abstract
Power system stability is managed through various control loops, including the Automatic Voltage Regulator (AVR), which regulates the terminal voltage of synchronous generators. This study integrated Fuzzy Logic Control (FLC) and a Proportional–Integral–Derivative controller with Filtered derivative action (PIDF) to propose a hybrid [...] Read more.
Power system stability is managed through various control loops, including the Automatic Voltage Regulator (AVR), which regulates the terminal voltage of synchronous generators. This study integrated Fuzzy Logic Control (FLC) and a Proportional–Integral–Derivative controller with Filtered derivative action (PIDF) to propose a hybrid Fuzzy PIDF controller enhanced by Fractional-Order Proportional-Derivative (FOPD) for AVR applications. For the first time, the newly introduced Sand Cat Swarm Optimization (SCSO) algorithm was applied to the AVR system to tune the parameters of the proposed fuzzy controller. The SCSO algorithm has been recognized as a powerful optimization tool and has demonstrated success across various engineering applications. The well-known Particle Swarm Optimization (PSO) algorithm was also utilized in this study to optimize the gains of the proposed controller. The Fuzzy PIDF plus FOPD is a novel configuration that is designed to be a robust control technique for AVR to achieve an excellent performance. In this research, the Fuzzy PIDF + FOPD controller was optimized using the PSO and SCSO algorithms by minimizing the Integral Time Absolute Error (ITAE) objective function to enhance the overall performance of AVR systems. A comparative analysis was conducted to evaluate the superiority of the proposed approach by benchmarking the results against those of other controllers reported in the literature. Furthermore, the robustness of the controller was assessed under parametric uncertainties and varying load disturbances. Also, its robustness was examined against disturbances in the control signal. The results demonstrate that the proposed Fuzzy PIDF + FOPD controller tuned by the PSO and SCSO algorithms delivers exceptional performance as an AVR controller, outperforming other controllers. Additionally, the findings confirm the robustness of the Fuzzy PIDF + FOPD controller against parametric uncertainties, establishing its potential for a successful implementation in real-time applications. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>AVR system structure.</p>
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<p>AVR system’s block diagram.</p>
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<p>Step response of the AVR system without a controller.</p>
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<p>Root locus diagram of the AVR system without a controller.</p>
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<p>The structure of the proposed AVR system.</p>
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<p>The membership functions of the fuzzy controller.</p>
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<p>Flowchart for the SCSO algorithm.</p>
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<p>The SCSO algorithm tunes the suggested Fuzzy PIDF plus FOPD structure for the AVR system.</p>
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<p>The convergence curves of the SCSO and PSO algorithms.</p>
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<p>The dynamic response of the AVR model based on different control techniques.</p>
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<p>Settling and rise times of different techniques.</p>
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<p>ITAE of different techniques.</p>
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<p>Peak overshoot and undershoot of different techniques.</p>
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<p>Step responses of AVR systems without a controller under different parametric uncertainty conditions.</p>
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<p>Step responses of AVR systems without a controller under different parametric uncertainty conditions.</p>
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<p>Step responses of AVR system in nominal conditions and under parametric uncertainties.</p>
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<p>The AVR system under control signal and load disturbances.</p>
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<p>Step responses of the AVR system in nominal conditions and under parametric uncertainties in addition to load and control signal disturbances.</p>
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22 pages, 7165 KiB  
Article
Instantaneous Frequency Analysis Based on High-Order Multisynchrosqueezing Transform on Motor Current and Application to RV Gearbox Fault Diagnosis
by Shiyi Chai and Kai Xu
Machines 2025, 13(3), 223; https://doi.org/10.3390/machines13030223 (registering DOI) - 8 Mar 2025
Viewed by 17
Abstract
Motor current analysis is useful for ensuring the safety and reliability of electromechanical systems. However, for gearboxes, the commonly used methods of detecting faulty frequency sidebands are easily disturbed by installation errors, inherent harmonics, and fundamental frequency with high amplitude. Aiming at this [...] Read more.
Motor current analysis is useful for ensuring the safety and reliability of electromechanical systems. However, for gearboxes, the commonly used methods of detecting faulty frequency sidebands are easily disturbed by installation errors, inherent harmonics, and fundamental frequency with high amplitude. Aiming at this problem, this study presents instantaneous frequency polarview (IFpolarview), which diagnoses faults based on motor angle and motor current frequency modulation (FM) features. Firstly, to address the problem of the limited analysis order of higher-order synchrosqueezing transform (HSST), the higher-order multisynchrosqueezing transform (HMSST) is introduced to improve the instantaneous frequency (IF) estimation accuracy and reveal the transient fault features from the motor current without further increasing the order and algorithm difficulty. Then, based on the motor angle and accurate motor current IF extracted from HMSST, the IFpolarview is proposed to visualize gear faults through detecting the FM of motor current synchronized with the faulty gear mesh. In the simulation, the IF estimation error of HMSST is 2.51%, which is smaller than other methods. The experimental results show that the HMSST has the smallest Rényi entropy value of 9.13, implying that the most aggregated time–frequency representation (TFR) of the energy is obtained. HMSST can enhance the resolution of fault characteristics, and IFpolarview concentrates the abnormal IF fluctuations with periodicity into a small angular interval, which highlights the fault features and demonstrates greater intuitiveness and reliability in comparison to the frequency sideband detection method. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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<p>Schematic view of IFpolarview.</p>
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<p>Flowchart of the proposed method.</p>
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<p>Simulation current signals: (<b>a</b>) motor current and (<b>b</b>) spectrum of motor current signal.</p>
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<p>TFRs of the simulated motor current: (<b>a</b>) STFT, (<b>b</b>) partial enlargement of (<b>a</b>), (<b>c</b>) SST, (<b>d</b>) partial enlargement of (<b>c</b>), (<b>e</b>) 2-SST, (<b>f</b>) partial enlargement of (<b>e</b>), (<b>g</b>) 4-HSST result, (<b>h</b>) partial enlargement of (<b>g</b>), (<b>i</b>) CWT result, (<b>j</b>) partial enlargement of (<b>i</b>).</p>
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<p>TFRs of the simulated motor current: (<b>a</b>) 3-MSST, (<b>b</b>) second-order 3-MSST, (<b>c</b>) [4,3]-HMSST, (<b>d</b>) partial enlargement of (<b>a</b>), (<b>e</b>) partial enlargement of (<b>b</b>), (<b>f</b>) partial enlargement of (<b>c</b>).</p>
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<p>IFpolarviews: (<b>a</b>) IF of 3-MSST, (<b>b</b>) IF of second-order 3-MSST, (<b>c</b>) IF of [4,3]-HMSST.</p>
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<p>Experimental setup: (<b>a</b>) servo joint test bench (<b>b</b>) sun gear single tooth root crack, (<b>c</b>) planetary gear single tooth root crack.</p>
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<p>Motor rotation speed and angle profile for one cycle: (<b>a</b>) motor speed (<b>b</b>) motor rotation angle.</p>
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<p>Motor current signals: (<b>a</b>) sun gear single tooth root crack, (<b>b</b>) planetary gear single tooth root crack.</p>
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<p>Spectrum of motor currents: (<b>a</b>) sun gear single tooth root crack, (<b>b</b>) planetary gear single tooth root crack.</p>
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<p>TFRs of the motor current of planetary gear single tooth root crack: (<b>a</b>) 3-MSST, (<b>b</b>) second-order 3-MSST, (<b>c</b>) [4,3]-HMSST, (<b>d</b>) partial enlargement of (<b>a</b>), (<b>e</b>) partial enlargement of (<b>b</b>), (<b>f</b>) partial enlargement of (<b>c</b>).</p>
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<p>IFs of the motor current of planetary gear single tooth root crack: (<b>a</b>) results of [4,3]-HMSST, second-order 3-MSST, and 3-MSST, (<b>b</b>) partial enlargement of (<b>a</b>).</p>
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<p>IFpolarview of planetary gear single tooth root crack.</p>
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<p>IF of the motor current of sun gear single tooth root crack: (<b>a</b>) results of [4,3]-HMSST, (<b>b</b>) partial enlargement of (<b>a</b>).</p>
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<p>IFpolarview of sun gear single tooth root crack.</p>
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<p>Current signal of fatigue RV gearbox: (<b>a</b>) current signal, (<b>b</b>) spectrum of current signal.</p>
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<p>IF of the motor current of fatigue RV gearbox: (<b>a</b>) results of [4,3]-HMSST, (<b>b</b>) partial enlargement of (<b>a</b>).</p>
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<p>IFpolarview of fatigue RV gearbox.</p>
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<p>Multi-tooth wear faults of planetary gear in fatigue RV reducer.</p>
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28 pages, 8592 KiB  
Article
Sensorless Control of Permanent Magnet Synchronous Motor Drives with Rotor Position Offset Estimation via Extended State Observer
by Ramón Ramírez-Villalobos, Luis N. Coria, Paul A. Valle and Christian Aldrete-Maldonado
Mathematics 2025, 13(6), 899; https://doi.org/10.3390/math13060899 - 7 Mar 2025
Viewed by 168
Abstract
The aim of this study is to develop sensorless high-speed tracking control for surface-mounted permanent magnet synchronous motors by taking the rotor position offset error and time-varying load torque into consideration. This proposal combines an extended state observer with an adaptation position algorithm, [...] Read more.
The aim of this study is to develop sensorless high-speed tracking control for surface-mounted permanent magnet synchronous motors by taking the rotor position offset error and time-varying load torque into consideration. This proposal combines an extended state observer with an adaptation position algorithm, employing only the measurement of electrical variables for feedback. First, a rotatory coordinate model of the motor is proposed, wherein the rotor position offset error is considered as a perturbation function within the model. Second, based on the aforementioned model, a rotary coordinate model of the motor is extended in one state to estimate the load torque, as well as the rotor’s position and speed, despite the presence of the rotor position offset error. Through Lyapunov stability analysis, sufficient conditions were established to guarantee that the error estimations were bounded. Finally, to validate the feasibility of the proposed sensorless scheme, experiments were conducted on the Technosoft® development platform, where the alignment routine was disabled and an intentional misalignment between the magnetic north pole and the stator’s south pole was established. Full article
(This article belongs to the Special Issue Nonlinear Dynamical Systems: Modeling, Control and Applications)
9 pages, 2036 KiB  
Proceeding Paper
PSO-Based PID Tuning for PMSM-Quadrotor UAV System
by Marco Rinaldi, Morteza Moslehi, Giorgio Guglieri and Stefano Primatesta
Eng. Proc. 2025, 90(1), 2; https://doi.org/10.3390/engproc2025090002 - 7 Mar 2025
Viewed by 53
Abstract
This paper presents the simulation and controller optimization of a quadrotor Unmanned Aerial Vehicle (UAV) system. The quadrotor model is derived adopting the Newton-Euler approach, and is intended to be constituted by four three-phase Permanent Magnet Synchronous Motors (PMSM) controlled with a velocity [...] Read more.
This paper presents the simulation and controller optimization of a quadrotor Unmanned Aerial Vehicle (UAV) system. The quadrotor model is derived adopting the Newton-Euler approach, and is intended to be constituted by four three-phase Permanent Magnet Synchronous Motors (PMSM) controlled with a velocity control loop-based Field Oriented Control (FOC) technique. The Particle Swarm Optimization (PSO) algorithm is used to tune the parameters of the PID controllers of quadrotor height, quadrotor attitude angles, and PMSMs’ rotational speeds, which represent the eight critical parameters of the PMSM-quadrotor UAV system. The PSO algorithm is designed to optimize eight Square Error (SE) cost functions which quantify the error dynamics of the controlled variables. For each stabilization task, the PID tuning is divided in two phases. Firstly, the PSO optimizes the error dynamics of altitude and attitude angles of the quadrotor UAV. Secondly, the desired steady-state rotational speeds of the PMSMs are derived, and the PSO is used to optimize the motors’ dynamics. Finally, the complete PMSM-Quadrotor UAV system is simulated for stabilization during the target task. The study is carried out by means of simulations in MATLAB/Simulink®. Full article
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<p>(<b>a</b>) Schematic representation of the quadrotor platform, taken from [<a href="#B17-engproc-90-00002" class="html-bibr">17</a>]. (<b>b</b>)Schematic representation of the PMSM system and its control unit.</p>
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<p>(<b>a</b>) Flow chart of the PSO algorithm based on [<a href="#B19-engproc-90-00002" class="html-bibr">19</a>]; (<b>b</b>) Process of searching for a new position in the PSO methodology; (<b>c</b>) Schematic representation of how the PSO framework is used for optimizing the PID parameters of both quadrotor and PMSMs’ controllers.</p>
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<p>Comparing (<b>a</b>) optimized and (<b>b</b>) non-optimized PMSM-Quadrotor UAV system’s performances for a hovering stabilization task. Comparing (<b>c</b>) optimized and (<b>d</b>) non-optimized PMSM-Quadrotor UAV system’s performances for a maneuvering stabilization task. Simulations performed with a set of random initial conditions.</p>
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<p>(<b>a</b>,<b>b</b>) Optimized dynamics of one motor during the maneuvering stabilization tasks, simulations refer to the same set of <a href="#engproc-90-00002-f003" class="html-fig">Figure 3</a>c. Comparing (<b>c</b>) optimized and (<b>d</b>) non-optimized dynamics of one motor for different reference velocities (i.e., different maneuvers).</p>
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19 pages, 7225 KiB  
Article
Utilization of MCU and Real-Time Simulator for Identifying Beatless Control for Six-Step Operation of Three-Phase Inverter
by Yongsu Han
Electronics 2025, 14(5), 1030; https://doi.org/10.3390/electronics14051030 - 5 Mar 2025
Viewed by 109
Abstract
In industries dealing with motor drive systems, the use of real-time simulators for validating control codes is becoming increasingly mandatory. This is particularly essential for systems with advanced control codes or complex microcontroller unit (MCU) register configurations, as this validation process helps prevent [...] Read more.
In industries dealing with motor drive systems, the use of real-time simulators for validating control codes is becoming increasingly mandatory. This is particularly essential for systems with advanced control codes or complex microcontroller unit (MCU) register configurations, as this validation process helps prevent accidents and shorten development time. This study presents a validation process using a real-time simulator for the beatless control of six-step operation. Six-step operation, when applied to high-speed drives, has a limitation on the number of samples per electrical rotation, which causes voltage errors. A representative of these voltage error phenomena is the beat phenomenon, resulting in torque ripple at the first harmonic and high current ripple. To mitigate this beat phenomenon, a synchronous PWM method is sometimes used. However, in practical industrial systems, it may not be feasible to synchronously adjust the inverter’s switching frequency with the rotation speed. This study proposes a beatless control method to eliminate the voltage errors caused by the beat phenomenon during six-step operation at a fixed switching frequency. The specific implementation of this control method is explained based on MCU timer register settings. While previous studies have only proposed beatless control methods, this paper goes further by implementing the proposed beatless method using the MCU (TMS320F28335) to generate gating signals and validating the implementation through simulation on a permanent magnet synchronous motor using a real-time simulator (Typhoon HIL). Full article
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<p>Three-phase motor drive system with an inverter and permanent magnet synchronous motor.</p>
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<p>Voltage hexagon by 3-phase inverter and switching states at six vertex points.</p>
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<p>Switching signals and phase voltages according to synchronous PWM in 6-step operation.</p>
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<p>Pole voltage error due to the beat phenomenon in 6-step operation. (<b>a</b>) PPR = 13, (<b>b</b>) PPR = 7.</p>
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<p>Phase voltage waveforms and FFT results. (<b>a</b>) PPR = 12, (<b>b</b>) PPR = 13.</p>
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<p>Implementation of carrier-based PWM using the MCU’s timer registers.</p>
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<p>Operating region setup and voltage reference modification method in 6-step operation region.</p>
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<p>Conventional carrier-based PWM method in 6-step region without beatless control.</p>
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<p>Voltage references considering the phase variation at the peak and valley points of the PWM carrier.</p>
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<p>Case determination based on the positions of V<sub>1</sub>, V<sub>2</sub>, and V<sub>3</sub> vectors of <span class="html-italic">v<sub>dqs</sub><sup>i</sup>*</span>. (<b>a</b>) Case I, (<b>b</b>) case II, (<b>c</b>) case III.</p>
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<p>Case determination based on the positions of V<sub>1</sub>, V<sub>2</sub>, and V<sub>3</sub> vectors of <span class="html-italic">v<sub>dqs</sub><sup>i</sup>*</span>. (<b>a</b>) Case I, (<b>b</b>) case II, (<b>c</b>) case III.</p>
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<p>Entire block diagram of the proposed 6-step beatless control.</p>
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<p>Experimental setup including the real-time simulator (Typhoon HIL) and the MCU (TMS320F28335).</p>
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<p>Circuit configuration for real-time simulation and speed setting method.</p>
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<p>(Real-time) Simulation results at 5500 [r/min] and 120 [Nm]: results before and after applying the proposed beatless control.</p>
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<p>(Real-time) Simulation results at 12,000 [r/min] and 45 [Nm]: results before and after applying the proposed beatless control.</p>
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23 pages, 9774 KiB  
Article
Predictive Torque Control of Permanent Magnet Motor for New-Energy Vehicles Under Low-Carrier-Ratio Conditions
by Zhiqiang Wang, Zhichen Lin, Xuefeng Jin and Yan Yan
World Electr. Veh. J. 2025, 16(3), 146; https://doi.org/10.3390/wevj16030146 - 4 Mar 2025
Viewed by 191
Abstract
The model predictive-torque-control strategy of a permanent magnet synchronous motor (PMSM) has many advantages such as a fast dynamic response and the ease of implementation. However, when the permanent magnet motor has a large number of pole pairs or operates at high-speed, due [...] Read more.
The model predictive-torque-control strategy of a permanent magnet synchronous motor (PMSM) has many advantages such as a fast dynamic response and the ease of implementation. However, when the permanent magnet motor has a large number of pole pairs or operates at high-speed, due to constraints such as the inverter switching frequency, sampling time, and algorithm execution time, the motor carrier ratio (the ratio of control frequency to operating frequency) becomes relatively low. The discrete model derived from and based on the forward Euler method has a large model error when the carrier ratio decreases, which leads to voltage vector misjudgment and inaccurate duty cycle calculation, thus leading to the decline of control performance. Meanwhile, the shortcomings of the traditional model predictive-torque-control strategy limit the steady-state performance. In response to the above issues, this paper proposes an improved model predictive-torque-control strategy suitable for low-carrier-ratio conditions. The strategy consists of an improved discrete model that considers rotor-angle-position variations and a model prediction algorithm. It also analyzes the sensitivity of model predictive control to parameter changes and designs an online parameter optimization algorithm. Compared with the traditional forward Euler method, the improved discrete model proposed in this paper has obvious advantages under low-carrier-ratio conditions; at the same time, the parameter optimization process enhances the parameter robustness of the model prediction algorithm. Moreover, the proposed model predictive-torque-control strategy has high torque tracking accuracy. The experimental results verify the feasibility and effectiveness of the proposed strategy. Full article
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<p>The block diagram of traditional model predictive torque control.</p>
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<p>The block diagram of vector action within a unit carrier period.</p>
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<p>The plot of torque trajectories for different vector combinations.</p>
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<p>The block diagram of the optimal vector action combination.</p>
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<p>The comparison chart of action vectors, three-phase switching states, and torque fluctuations per unit carrier period with the improved sampling and duty cycle update strategy.</p>
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<p>The flow chart of the model reference adaptive-parameter-optimization algorithm.</p>
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<p>General block diagram of the control strategy proposed in this paper.</p>
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<p>The comparison waveforms of the current, stator flux, and torque of the predictive model at low speed.</p>
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<p>The comparison waveforms of the current, stator flux, and torque of the predictive model at medium speed.</p>
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<p>The improved prediction model current, stator flux, and torque waveform at high speed.</p>
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<p>The waveform of parameter optimization link current, stator flux, and torque.</p>
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<p>The comparison of difference in E before and after optimization.</p>
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<p>The current THD analysis of parameter normal, parameter mismatch, and optimization completion.</p>
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<p>The plot of model predictive-torque-control strategy current, stator flux and torque waveforms at low speed.</p>
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<p>The plot of model predictive-torque-control strategy current, stator flux, and torque waveforms at medium speed.</p>
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<p>The plot of model predictive-torque-control strategy current, stator flux and torque waveforms at high speed.</p>
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18 pages, 4125 KiB  
Article
An Improved Second-Order Generalized Integrator Phase-Locked Loop with Frequency Error Compensation
by Zhaoyang Yan, Hanyi Qiao, Zongze Guo, Dongxu Wang and Yidan Feng
Electronics 2025, 14(5), 1018; https://doi.org/10.3390/electronics14051018 - 3 Mar 2025
Viewed by 182
Abstract
In distributed energy grid-connected systems, fast and accurate grid synchronization technology is crucial for system stability. This article proposes an improved phase-locked loop (FECSOGI-PLL) based on frequency error compensation. By introducing an unbiased adaptive frequency compensation mechanism, the SOGI resonant frequency is adjusted [...] Read more.
In distributed energy grid-connected systems, fast and accurate grid synchronization technology is crucial for system stability. This article proposes an improved phase-locked loop (FECSOGI-PLL) based on frequency error compensation. By introducing an unbiased adaptive frequency compensation mechanism, the SOGI resonant frequency is adjusted in real time to accurately track the input signal. A linear time invariant (LTI) model of the FECSOGI-PLL was established in the article, and its wider stability domain was clarified based on the Routh–Hurwitz criterion. The strong robustness of its fast response under non-ideal conditions, such as frequency jumps and amplitude drops, was verified through simulation and experiments. The core innovation of this study lies in the first implementation of unbiased adaptive regulation of the SOGI resonant frequency through the frequency error compensation mechanism, as well as the system design method based on the extended stability domain, providing theoretical support and engineering practice reference for high robustness power grid synchronization technology. Full article
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<p>SOGI-QSG structure diagram.</p>
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<p>The relationship between the Bode plots of <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mi>α</mi> </msub> <mo stretchy="false">(</mo> <mi mathvariant="normal">s</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mi>β</mi> </msub> <mo stretchy="false">(</mo> <mi mathvariant="normal">s</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> with the variation of system gain. (<b>a</b>) The Bode plot of <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mi>α</mi> </msub> <mo stretchy="false">(</mo> <mi mathvariant="normal">s</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math>. (<b>b</b>) The Bode plot of <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mi>β</mi> </msub> <mo stretchy="false">(</mo> <mi mathvariant="normal">s</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math>.</p>
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<p>SOGI-PLL structure block diagram.</p>
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<p>Structure block diagram of FECSOGI-PLL.</p>
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<p>Waveform diagram of the feedback SOGI resonant frequency <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>ω</mi> <mo>^</mo> </mover> <mi>s</mi> </msub> </mrow> </semantics></math>.</p>
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<p>LTI model block diagrams of FECSOGI-PLL and SOGI-PLL, where <math display="inline"><semantics> <mrow> <mi>Z</mi> <mo>=</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mrow> <mi>k</mi> <msub> <mi>ω</mi> <mi>n</mi> </msub> </mrow> <mn>2</mn> </mfrac> </mstyle> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>P</mi> <mo>=</mo> <msub> <mi>k</mi> <mi>p</mi> </msub> <msub> <mi>V</mi> <mi>n</mi> </msub> <mo>+</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mrow> <msub> <mi>k</mi> <mi>i</mi> </msub> <msub> <mi>V</mi> <mi>n</mi> </msub> </mrow> <mi>s</mi> </mfrac> </mstyle> </mrow> </semantics></math>. (<b>a</b>) The LTI model of the FECSOGI-PLL. (<b>b</b>) The LTI model of the SOGI-PLL.</p>
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<p>Comparison of LTI model outputs between the two under the same <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>p</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>i</mi> </msub> </mrow> </semantics></math> conditions. (<b>a</b>) Case 1. (<b>b</b>) Case 2.</p>
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<p>Waveforms of DQ-axis outputs for SOGI-PLL and FECSOGI-PLL under input voltage frequency step changes. (<b>a</b>) DQ-axis output waveforms under input voltage frequency step changes. (<b>b</b>) DQ-axis output waveforms under simultaneous input amplitude and frequency step changes.</p>
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<p>Experiential setup.</p>
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<p>Power grid frequency mutation. (<b>a</b>) Output phase angle of the SOGI-PLL and FECSOGI-PLL. (<b>b</b>) DQ-axis output of the SOGI-PLL and FECSOGI-PLL. (<b>c</b>) Frequency output waveforms of SOGI-PLL, FECSOGI-PLL, APF-PLL, FFSOGI-PLL.</p>
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<p>Simultaneous mutation of power grid frequency and amplitude. (<b>a</b>) Output phase angle of the SOGI-PLL and FECSOGI-PLL. (<b>b</b>) DQ-axis output of the SOGI-PLL and FECSOGI-PLL. (<b>c</b>) Frequency output waveforms of SOGI-PLL, FECSOGI-PLL, APF-PLL, FFSOGI-PLL.</p>
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<p>(<b>a</b>) Frequency output waveforms of SOGI-PLL, FECSOGI-PRL, APF-PLL, and FFSOGI-PRL under small frequency disturbances. (<b>b</b>) Frequency output waveforms of SOGI-PLL, FECSOGI-PLL, APF-PLL, and FFSOGI-PLL under the frequency jump of adding DC bias.</p>
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19 pages, 35731 KiB  
Article
Robust Synchronization Error Estimation Under Multipath Fading in Distributed SAR
by Jihao Xin, Xingdong Liang, Zhiyu Jiang, Hang Li, Yujie Dai, Huan Wang, Yuan Zhang and Xiangxi Bu
Electronics 2025, 14(5), 983; https://doi.org/10.3390/electronics14050983 - 28 Feb 2025
Viewed by 260
Abstract
Unmanned Aerial Vehicle (UAV)-based distributed Synthetic Aperture Radar (SAR) is a current research focus. Phase synchronization is crucial for eliminating the non-coherence of distributed systems. However, as the number of UAVs increases, fast time-varying multipath effects caused by rotors can lead to multipath [...] Read more.
Unmanned Aerial Vehicle (UAV)-based distributed Synthetic Aperture Radar (SAR) is a current research focus. Phase synchronization is crucial for eliminating the non-coherence of distributed systems. However, as the number of UAVs increases, fast time-varying multipath effects caused by rotors can lead to multipath fading. This degrades the signal-to-noise ratio (SNR) of the synchronization link and distorts the synchronization waveform. It further breaks the reciprocity of the dual one-way synchronization link, ultimately degrading phase synchronization accuracy. We propose a robust method for spike detection and error propagation to improve phase synchronization precision. Using the Hampel filter, we detect pulse peak position jitter and remove observations from anomalous links. We then use data fusion based on minimum variance to recover synchronization errors in these links, leveraging the redundancy in synchronization phase matrices. The effectiveness of the proposed method is confirmed through flight test data from a four-UAV distributed TomoSAR experiment. Compared to the maximum-peak detection method, the phase accuracy is improved from 12.84 deg to 0.61 deg. This method supports the application of distributed SAR. Full article
(This article belongs to the Special Issue New Challenges in Remote Sensing Image Processing)
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<p>The TDMA synchronization scheme applied for the distributed SAR imaging.</p>
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<p>The general shape of the Allan variance. (<b>a</b>) The Modified Allan variance (<math display="inline"><semantics> <mrow> <mi>M</mi> <mi>o</mi> <mi>d</mi> <msubsup> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>φ</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msubsup> <mfenced separators="|"> <mrow> <mi>τ</mi> </mrow> </mfenced> </mrow> </semantics></math>) of an oscillator. (<b>b</b>) The Allan variance (<math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>φ</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msubsup> <mfenced separators="|"> <mrow> <mi>τ</mi> </mrow> </mfenced> </mrow> </semantics></math>) of the synchronization phase, which includes the inherent instability of the oscillator and the measurement error.</p>
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<p>Illustration of multipath in multi-node synchronization links.</p>
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<p>The multipath fading effect after pulse compression. (<b>a</b>) The LOS pulse compression waveform compared with the multipath fading waveform; (<b>b</b>) the residual time and phase errors.</p>
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<p>The Monte Carlo simulation for the different SNRs after pulse compression.</p>
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<p>(<b>a</b>) Optical image; (<b>b</b>) configuration of distributed TomoSAR with 4 UAV SARs.</p>
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<p>Matched filter outputs of sync signals of different UAVs during flight.</p>
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<p>Unprocessed synchronization phase and first-order difference in all links. (<b>a</b>) Link <span class="html-italic">AB</span>; (<b>b</b>) Link <span class="html-italic">AC</span>; (<b>c</b>) Link <span class="html-italic">AD</span>; (<b>d</b>) Link <span class="html-italic">BC</span>; (<b>e</b>) Link <span class="html-italic">BD</span>; (<b>f</b>) Link <span class="html-italic">CD</span>.</p>
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<p>Unprocessed synchronization phase and first-order difference in all links. (<b>a</b>) Link <span class="html-italic">AB</span>; (<b>b</b>) Link <span class="html-italic">AC</span>; (<b>c</b>) Link <span class="html-italic">AD</span>; (<b>d</b>) Link <span class="html-italic">BC</span>; (<b>e</b>) Link <span class="html-italic">BD</span>; (<b>f</b>) Link <span class="html-italic">CD</span>.</p>
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<p>The Modified Allan variances (MVARs) of the synchronization phase for the different links. (<b>a</b>) The MVAR for all the 6 direct links; (<b>b</b>) the MVAR for Link <span class="html-italic">BD</span> from five different paths.</p>
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<p>The 32-times-interpolated pulse compression signal slices from the synchronization links, normalized by each maximum value and centered on the maximum: (<b>a</b>) the link between UAVs <span class="html-italic">A</span> and <span class="html-italic">B</span>; (<b>b</b>) the link between UAVs <span class="html-italic">A</span> and <span class="html-italic">C</span>; (<b>c</b>) the link between UAVs <span class="html-italic">A</span> and <span class="html-italic">D</span>; (<b>d</b>) the link between UAVs <span class="html-italic">B</span> and <span class="html-italic">C</span>; (<b>e</b>) the link between UAVs <span class="html-italic">B</span> and <span class="html-italic">D</span>, where the maximum peaks are misaligned; (<b>f</b>) the link between UAVs <span class="html-italic">C</span> and <span class="html-italic">D</span>.</p>
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<p>The spike detection results. (<b>a</b>) The Hampel filter output of the link <span class="html-italic">BD</span>; (<b>b</b>) the number of effective links, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="bold-italic">R</mi> </mrow> <mrow> <mi mathvariant="bold-italic">n</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>B</mi> <mo>,</mo> <mi>D</mi> </mrow> </mfenced> </mrow> </semantics></math>, calculated by Equation (13).</p>
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<p>The performance of synchronization phase estimations. (<b>a</b>) The synchronization phase estimations and first-order differences (FODs). (<b>b</b>) A statistical histogram of the FODs of the synchronization phase using the max-peak position method and the proposed method.</p>
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<p>The Modified Allan variances of the synchronization phases.</p>
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<p>Imaging results of BiSAR echo from UAVs <span class="html-italic">B</span> and <span class="html-italic">D</span>, with labels “<span class="html-italic">A</span>” and “<span class="html-italic">B</span>” indicating corner reflectors <span class="html-italic">A</span> and scatter <span class="html-italic">B</span>, respectively. (<b>a</b>) SAR image compensated with unprocessed synchronization phase; (<b>b</b>) SAR image compensated with processed synchronization phase.</p>
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<p>Magnified SAR image containing <span class="html-italic">A</span> and <span class="html-italic">B</span> (16× up-sampling): (<b>a</b>) corner reflector <span class="html-italic">A</span> compensated with unprocessed synchronization phase; (<b>b</b>) corner reflector <span class="html-italic">A</span> compensated with processed synchronization phase; (<b>c</b>) scatter <span class="html-italic">B</span> compensated with unprocessed synchronization phase; (<b>d</b>) scatter <span class="html-italic">B</span> compensated with processed synchronization phase.</p>
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28 pages, 2968 KiB  
Article
A Novel Azimuth Channel Errors Estimation Algorithm Based on Characteristic Clusters Statistical Treatment
by Wensen Yang, Ran Tao, Hao Huan, Jing Feng, Longyong Chen, Yihao Xu and Junhua Yang
Remote Sens. 2025, 17(5), 857; https://doi.org/10.3390/rs17050857 - 28 Feb 2025
Viewed by 157
Abstract
Azimuth multi-channel techniques show great promise in high-resolution, wide-swath synthetic aperture radar systems. However, in practical engineering applications, errors between channels can significantly affect the reconstruction of multi-channel echo data, leading to a degraded synthetic aperture radar image. To address this issue, this [...] Read more.
Azimuth multi-channel techniques show great promise in high-resolution, wide-swath synthetic aperture radar systems. However, in practical engineering applications, errors between channels can significantly affect the reconstruction of multi-channel echo data, leading to a degraded synthetic aperture radar image. To address this issue, this article derives the formula expression in the two-dimensional time domain after single-channel processing under the assumption of an insufficient azimuth sampling rate and proposes a novel algorithm based on the statistical treatment of characteristic clusters. In this algorithm, channel imaging is first performed separately; then, the image is divided into a predefined number of sub-images. The characteristic clusters and points within each sub-image are identified, and their positions, amplitude, and phase information are used to obtain the range synchronization errors, amplitude errors, and phase errors between channels. Compared with traditional methods, the proposed method does not require iteration or the complex eigenvalue decomposition of the covariance matrix. Furthermore, it can utilize existing imaging tools and software in single-channel synthetic aperture radar systems. The effectiveness of the proposed method is validated through simulation experiments and real-world data processing. Full article
(This article belongs to the Special Issue Microwave Remote Sensing for Object Detection 2nd Edition)
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<p>Geometric diagram of a multichannel HRWS-SAR system and a static ground-based characteristic cluster.</p>
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<p>Processing flowchart of the proposed method.</p>
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<p>Matched filtering results of the five-channel SAR system’s Channel 1 before reconstruction with different PRF values: (<b>a</b>) Sampling at PRF = 10 Hz and the corresponding ambiguity index is 7. (<b>b</b>) Sampling at PRF = 17 Hz and the corresponding ambiguity index is 5. (<b>c</b>) Sampling at PRF = 30 Hz and the corresponding ambiguity index is 3. (<b>d</b>) Sampling at PRF = 50 Hz and the corresponding ambiguity index is 3.</p>
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<p>Matched filtering results of the five-channel SAR system’s Channel 1 before reconstruction with different PRF values: (<b>a</b>) Sampling at PRF = 10 Hz and the corresponding ambiguity index is 7. (<b>b</b>) Sampling at PRF = 17 Hz and the corresponding ambiguity index is 5. (<b>c</b>) Sampling at PRF = 30 Hz and the corresponding ambiguity index is 3. (<b>d</b>) Sampling at PRF = 50 Hz and the corresponding ambiguity index is 3.</p>
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<p>Matched filtering results of the five-channel SAR system’s single channels before reconstruction: (<b>a</b>) Matched filtering result of channel 1. (<b>b</b>) Matched filtering result of channel 2. (<b>c</b>) Matched filtering result of channel 3. (<b>d</b>) Matched filtering result of channel 4. (<b>e</b>) Matched filtering result of channel 5.</p>
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<p>Matched filtering results of the five-channel SAR system’s single channels before reconstruction: (<b>a</b>) Matched filtering result of channel 1. (<b>b</b>) Matched filtering result of channel 2. (<b>c</b>) Matched filtering result of channel 3. (<b>d</b>) Matched filtering result of channel 4. (<b>e</b>) Matched filtering result of channel 5.</p>
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<p>Doppler spectrum and reconstructed echo results of a five-channel SAR system: (<b>a</b>) Doppler spectrum after reconstruction without channel error calibration. (<b>b</b>) Doppler spectrum after reconstruction using the proposed calibration method. (<b>c</b>) Reconstructed echo result without channel error calibration. (<b>d</b>) Reconstructed echo result with channel error calibration using the proposed method.</p>
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<p>Reconstruction performance using different channel error estimation methods: (<b>a</b>) Estimated results using the Cross-Correlation (CC) algorithm in the spatial time domain. (<b>b</b>) Estimated results using the Sub-Space (SS) algorithm in the Doppler frequency domain. (<b>c</b>) Estimated results using the LLN algorithm in the image domain. (<b>d</b>) Estimated results using the proposed method.</p>
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<p>Reconstruction performance using different channel error estimation methods: (<b>a</b>) Estimated results using the Cross-Correlation (CC) algorithm in the spatial time domain. (<b>b</b>) Estimated results using the Sub-Space (SS) algorithm in the Doppler frequency domain. (<b>c</b>) Estimated results using the LLN algorithm in the image domain. (<b>d</b>) Estimated results using the proposed method.</p>
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<p>The relationship between the phase error estimation ARMSE and SNR.</p>
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<p>Model of multi-channel phased-array antenna.</p>
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<p>Airplane mounted with multi-channel SAR system.</p>
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<p>Selected imaging scenes from Google Earth.</p>
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<p>Critical step results in processing. (<b>a</b>) Separately imaged results before construction of channel 1. (<b>b</b>) <math display="inline"><semantics> <mrow> <mn>4</mn> <mo>∗</mo> <mn>4</mn> <mo>=</mo> <mn>16</mn> </mrow> </semantics></math> sub-images zoning along azimuth and range directions. (<b>c</b>) Characteristic clusters searching and marking in 16 sub-images.</p>
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<p>Critical step results in processing. (<b>a</b>) Separately imaged results before construction of channel 1. (<b>b</b>) <math display="inline"><semantics> <mrow> <mn>4</mn> <mo>∗</mo> <mn>4</mn> <mo>=</mo> <mn>16</mn> </mrow> </semantics></math> sub-images zoning along azimuth and range directions. (<b>c</b>) Characteristic clusters searching and marking in 16 sub-images.</p>
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<p>Imaging results of the airborne SAR data using the proposed method.</p>
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<p>Detailed image of the airborne SAR data using the proposed method.</p>
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23 pages, 1930 KiB  
Article
Event-Driven Prescribed-Time Tracking Control for Multiple UAVs with Flight State Constraints
by Xueyan Han, Peng Yu, Maolong Lv, Yuyuan Shi and Ning Wang
Machines 2025, 13(3), 192; https://doi.org/10.3390/machines13030192 - 27 Feb 2025
Viewed by 84
Abstract
Consensus tracking control for multiple UAVs demonstrates critical theoretical value and application potential, improving system robustness and addressing challenges in complex operational environments. This paper addresses the challenge of event-triggered prescribed-time synchronization tracking control for 6-DOF fixed-wing UAVs with state constraints. We propose [...] Read more.
Consensus tracking control for multiple UAVs demonstrates critical theoretical value and application potential, improving system robustness and addressing challenges in complex operational environments. This paper addresses the challenge of event-triggered prescribed-time synchronization tracking control for 6-DOF fixed-wing UAVs with state constraints. We propose a novel prescribed-time command filtered backstepping approach to effectively tackle the issues of complexity explosion and singularities. By utilizing a state-transition function, we manage asymmetric time-varying state constraints, including limitations on speed, roll, yaw, and pitch angles in UAVs. The theoretical analysis demonstrates that all signals in the 6-DOF UAV system remain bounded, with tracking errors converging to the origin within the prescribed time. Finally, simulation results validate the effectiveness of the proposed control strategy. Full article
(This article belongs to the Special Issue Intelligent Control Techniques for Unmanned Aerial Vehicles)
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<p>Communication topology.</p>
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<p>The trajectory of speed and attitude and their tracking errors in proposed controller. (<b>a</b>) Speed response. (<b>b</b>) Roll−angle response. (<b>c</b>) Pitch−angle response. (<b>d</b>) Yaw−angle response. (<b>e</b>) Tracking error of speed. (<b>f</b>) Tracking error of roll angle. (<b>g</b>) Tracking error of pitch angle. <b>(h</b>) Tracking error of yaw angle.</p>
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<p>Speed and attitude tracking response under comparative controller and proposed controller. (<b>a</b>) Speed response. (<b>b</b>) Roll−angle response. (<b>c</b>) Pitch−angle response. (<b>d</b>) Yaw−angle response.</p>
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<p>Triggering time interval. (<b>a</b>) Data <math display="inline"><semantics> <msub> <mi>T</mi> <mrow> <mi>x</mi> <mn>1</mn> </mrow> </msub> </semantics></math>. (<b>b</b>) Data <math display="inline"><semantics> <msub> <mi>δ</mi> <mn>1</mn> </msub> </semantics></math>. (<b>c</b>) Data <math display="inline"><semantics> <msub> <mi>T</mi> <mrow> <mi>x</mi> <mn>2</mn> </mrow> </msub> </semantics></math>. (<b>d</b>) Data <math display="inline"><semantics> <msub> <mi>δ</mi> <mn>2</mn> </msub> </semantics></math>. (<b>e</b>) Data <math display="inline"><semantics> <msub> <mi>T</mi> <mrow> <mi>x</mi> <mn>3</mn> </mrow> </msub> </semantics></math>. (<b>f</b>) Data <math display="inline"><semantics> <msub> <mi>δ</mi> <mn>3</mn> </msub> </semantics></math>.</p>
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<p>The number of times an event was triggered.</p>
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24 pages, 8269 KiB  
Article
Compact Multi-Channel Long-Wave Wideband Direction-Finding System and Direction-Finding Analysis for Different Modulation Signals
by Hangyu Lu, Shun Wang, Xin Xu, Yicai Ji and Xiaojun Liu
Appl. Sci. 2025, 15(5), 2570; https://doi.org/10.3390/app15052570 - 27 Feb 2025
Viewed by 175
Abstract
This paper presents an optimized long-wave (10–300 kHz) wideband direction-finding system for scientific research. The antenna unit of the system comprises one vertical electric field sensor and two horizontal magnetic field sensors oriented in the north–south and east–west directions, respectively. The overall design [...] Read more.
This paper presents an optimized long-wave (10–300 kHz) wideband direction-finding system for scientific research. The antenna unit of the system comprises one vertical electric field sensor and two horizontal magnetic field sensors oriented in the north–south and east–west directions, respectively. The overall design prioritizes compactness, engineering feasibility, and ease of deployment, enabling the effective reception of long-wave radio signals within the 10–300 kHz range. The magnetic field sensitivity reaches 8fT/Hz@10kHz, while the electric field sensitivity achieves 3.2μV/m/Hz@10kHz. The overall sensitivity of the receiver is 1μV (300 Hz bandwidth, 10 dB signal-to-noise ratio). The synchronization accuracy of the system is within 10 ns. Theoretically, with a baseline length of 5 km and a signal incidence angle ranging from 9.9° to 170.1°, the direction finding error is less than 2°. Additionally, direction-finding methods for MSK and ASK modulated signals are analyzed. To evaluate the system’s actual performance, initial measurements were conducted in Qingdao, Shandong. Full article
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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<p>Information flow diagram of the long-wave wideband direction-finding system.</p>
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<p>Schematic of the E-field and B-field sensors.</p>
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<p>Equivalent circuit diagram of the flux feedback type broadband inductive magnetic field sensor.</p>
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<p>Conversion factor of the inductive magnetic field sensor.</p>
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<p>Schematic diagram of the electric field sensor working principle.</p>
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<p>Schematic of parallel-plate capacitor and low-noise amplifier circuit.</p>
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<p>Equivalent noise level of the sensor.</p>
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<p>Magnetic field antenna noise test results: 765 nV at 10 kHz, with a conversion factor of 100 mV/nT, corresponding to a noise level of <math display="inline"><semantics> <mrow> <mn>7.65</mn> <mi>f</mi> <mi>T</mi> <mo>/</mo> <msqrt> <mi>Hz</mi> </msqrt> </mrow> </semantics></math>.</p>
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<p>Electric field antenna noise test results: <math display="inline"><semantics> <mrow> <mn>3.2</mn> <mspace width="0.166667em"/> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">V</mi> </mrow> </semantics></math> at 10 kHz, with a conversion factor of 1 V/m, corresponding to a noise level of <math display="inline"><semantics> <mrow> <mn>3.2</mn> <mspace width="0.166667em"/> <mi mathvariant="sans-serif">μ</mi> <mrow> <mi mathvariant="normal">V</mi> <mo>/</mo> <mi mathvariant="normal">m</mi> </mrow> <msqrt> <mi>Hz</mi> </msqrt> </mrow> </semantics></math>.</p>
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<p>Time-frequency transmission principle diagram of Beidou co-viewing.</p>
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<p>Schematic diagram of the one-dimensional interferometric measurement principle.</p>
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<p>Distribution of direction finding error <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>α</mi> </msub> </semantics></math> with incident angle <math display="inline"><semantics> <mi>α</mi> </semantics></math>.</p>
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<p>Position distribution of transmitter and receiver and electromagnetic propagation path.</p>
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<p>Time-frequency spectrum of three channels within one minute.</p>
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<p>Power spectral density of three antennas of the main receiver within one minute.</p>
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<p>MSK demodulation results of the main receiver for the JJI (22.2 kHz) signal.</p>
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<p>Phase analysis of signals from main and reference receivers within five seconds in the JJI (22.2 kHz) signal bandwidth.</p>
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<p>Phase analysis of signals from main and reference receivers within five seconds in the JJY60 (60.0 kHz) signal bandwidth.</p>
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20 pages, 6422 KiB  
Article
Development of BIM Platform for Semantic Data Based on Standard WBS Codes
by Dongwook Kim, Jose Matos and Son N. Dang
Buildings 2025, 15(5), 711; https://doi.org/10.3390/buildings15050711 - 23 Feb 2025
Viewed by 311
Abstract
Building Information Modeling (BIM) has become an indispensable tool for risk management and construction oversight, especially in the case of complex and irregularly shaped buildings. BIM’s ability to reduce construction errors has been proven through advanced features like clash detection, schedule forecasting, and [...] Read more.
Building Information Modeling (BIM) has become an indispensable tool for risk management and construction oversight, especially in the case of complex and irregularly shaped buildings. BIM’s ability to reduce construction errors has been proven through advanced features like clash detection, schedule forecasting, and cost estimation. As the adoption of BIM grows, software providers such as Autodesk, Bentley, Trimble, and Nemetschek have developed advanced tools that incorporate Project Lifecycle Management (PLM). However, these tools are not easily transferable to Asian countries, where construction management often uses unit pricing rather than the more intricate systems common in Europe and the US. Legacy data also play a crucial role in Asian construction management, impacting risk profiling and cost predictions for similar projects. This study explores the integration of 4D BIM data within a Work Breakdown Structure (WBS) framework in a real-world setting. The first step was the creation of an in-house BIM platform, CEV (Civil Easy View), built on the Autodesk Forge viewer. CEV is designed as a BIM viewer tailored for field staff and supervisors. This 4D BIM application showed strong connectivity through standardized WBS codes, allowing for automatic synchronization between object and schedule data. Full article
(This article belongs to the Special Issue Built Environments and Environmental Buildings)
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<p>Development stages in the construction management BIM platform roadmap.</p>
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<p>Workflow for BIM platform methodology.</p>
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<p>Standard WBS of DL E&amp;C civil engineering division.</p>
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<p>Example of extended WBS codes (in red).</p>
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<p>Definition of extended code rules.</p>
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<p>CEV main platform.</p>
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<p>CEV attribute data based on WBS code.</p>
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<p>Dynamo logic for BIM model alignment.</p>
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<p>WBS Code input in Revit.</p>
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<p>BIM 360 folder tree for CEV.</p>
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<p>History management of BIM project.</p>
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<p>BIM model comparison according to model version.</p>
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22 pages, 2706 KiB  
Article
DMR-SCL: A Design and Verification Framework for Redundancy-Based Resilient Asynchronous Sleep Convention Logic Circuits
by Mithun Datta, Dipayan Mazumder, Alexander C. Bodoh and Ashiq A. Sakib
Electronics 2025, 14(5), 884; https://doi.org/10.3390/electronics14050884 - 23 Feb 2025
Viewed by 296
Abstract
The digital integrated circuit (IC) design industry is continuously evolving. However, the rapid advancements in technology are accompanied by major reliability concerns. Conventional clock-based synchronous designs become exceedingly susceptible to transient errors, caused by radiation rays, power jitters, electromagnetic interferences (EMIs), and/or other [...] Read more.
The digital integrated circuit (IC) design industry is continuously evolving. However, the rapid advancements in technology are accompanied by major reliability concerns. Conventional clock-based synchronous designs become exceedingly susceptible to transient errors, caused by radiation rays, power jitters, electromagnetic interferences (EMIs), and/or other noise sources, primarily due to aggressive device and voltage scaling. quasi-delay-insensitive (QDI) asynchronous (clockless) circuits demonstrate inherent robustness against such transient errors, owing to their unique architecture. However, they are not completely immune. This article presents a hardened QDI Sleep Convention Logic (SCL) asynchronous architecture, which can fully recover from radiation-induced single-event effects such as single-event upset (SEU) and single-event latch-up (SEL). Multiple benchmark circuits are designed based on the proposed architecture. The simulation results indicate that the proposed designs offer substantial energy savings per operation, dissipate substantially less power during idle phases, and have lower area footprints in comparison to designs based on an existing resilient Null Convention Logic (NCL) architecture at the cost of increased latency. In addition, a formal verification framework for the proposed architecture is also presented. The performance and scalability of the proposed verification scheme are demonstrated using several multiplier benchmark circuits of varying width. Full article
(This article belongs to the Section Circuit and Signal Processing)
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<p>(<b>a</b>) SCL SECRII framework. (<b>b</b>) Internal structure of an ICD unit.</p>
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<p>SEL/SEU-affected SCL pipeline: a visual representation illustrating how the SCL pipeline’s liveliness can be compromised.</p>
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<p>Proposed SEU/SEL-tolerant SCL framework.</p>
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<p>(<b>a</b>) The <span class="html-italic">Rail<sup>0</sup></span> network of a traditional SCL register, (<b>b</b>) the <span class="html-italic">Rail<sup>0</sup></span> network of the modified SCL register, (<b>c</b>) a traditional SCL TH23 gate structure, (<b>d</b>) a modified SCL TH23 gate structure. (<b>e</b>) A positive glitch in the <span class="html-italic">sleep</span> signal that affects the traditional SCL TH23 structure in (<b>d</b>), and (<b>f</b>) how the modified structure can address the corruption of a single <span class="html-italic">sleep</span> signal.</p>
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<p>SEL recovery procedure without generating incorrect outputs under scenario 1.</p>
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<p>SEL recovery procedure without generating incorrect outputs under scenario 2.</p>
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<p>SEL recovery procedure without causing deadlock.</p>
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<p>DMR-SCL 3 × 3 unsigned multiplier.</p>
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15 pages, 1736 KiB  
Article
Mathematical Models of Critical Soft Error in Synchronous and Self-Timed Pipeline
by Igor Sokolov, Yuri Stepchenkov, Yuri Diachenko and Dmitry Khilko
Mathematics 2025, 13(5), 695; https://doi.org/10.3390/math13050695 - 21 Feb 2025
Viewed by 240
Abstract
This paper analyzes the impact of a single soft error on the performance of a synchronous and self-timed pipeline. A nuclear particle running through the integrated circuit body is considered the most probable soft error source. The existing estimates show that self-timed circuits [...] Read more.
This paper analyzes the impact of a single soft error on the performance of a synchronous and self-timed pipeline. A nuclear particle running through the integrated circuit body is considered the most probable soft error source. The existing estimates show that self-timed circuits offer an advantage in terms of single soft error tolerance. The paper proves these estimates on the basis of a comparative probability analysis of a critical fault in two types of pipelines. The mathematical models derived in the paper describe the probability of a critical fault depending on the circuit’s characteristics, its operating discipline, and the soft error parameters. The self-timed pipeline operates in accordance with a two-phase discipline, based on the request–acknowledge interaction within the pipeline’s stages, which provides it with increased immunity to soft errors. Quantitative calculations performed on the basis of the derived mathematical models show that the self-timed pipeline has about 6.1 times better tolerance to a single soft error in comparison to its synchronous counterpart. The obtained results are in good agreement with empirical estimates of the soft error tolerance level of synchronous and self-timed circuits. Full article
(This article belongs to the Special Issue Reliability Estimation and Mathematical Statistics)
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<p>Block diagram of a synchronous pipeline.</p>
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<p>Block diagram of a self-timed pipeline.</p>
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<p>Condition <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mi>S</mi> </msub> <mo>+</mo> <msub> <mi>t</mi> <mrow> <mi>D</mi> <mi>S</mi> </mrow> </msub> <mo>&lt;</mo> <msub> <mi>T</mi> <mi>C</mi> </msub> </mrow> </semantics></math> probability calculation.</p>
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<p>Condition <span class="html-italic">T<sub>C</sub></span> &gt; <span class="html-italic">t<sub>S</sub> + t<sub>DS</sub></span> probability calculation (crosshatched region).</p>
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<p>Critical SE probability vs. SE duration distribution function variance <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mrow> <mi>S</mi> <mi>E</mi> </mrow> </msub> </mrow> </semantics></math> at <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mrow> <mi>S</mi> <mi>E</mi> </mrow> </msub> </mrow> </semantics></math> = 1 ns.</p>
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<p>Critical SE probability vs. SE duration distribution function mathematical expectation <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mrow> <mi>S</mi> <mi>E</mi> </mrow> </msub> </mrow> </semantics></math> at <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mrow> <mi>S</mi> <mi>E</mi> </mrow> </msub> </mrow> </semantics></math> = 0.4 ns.</p>
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Article
Sensorless Control of Ultra-High-Speed PMSM via Improved PR and Adaptive Position Observer
by Xiyue Bai, Weiguang Huang, Chuang Gao and Yingna Wu
Sensors 2025, 25(5), 1290; https://doi.org/10.3390/s25051290 - 20 Feb 2025
Viewed by 322
Abstract
To improve the precision of the position and speed estimation in ultra-high-speed (UHS) permanent magnet synchronous motors (PMSM) without position sensors, multiple refinements to the traditional extended electromotive force (EEMF) estimation algorithm are proposed in this paper. The key improvements include discretization compensation, [...] Read more.
To improve the precision of the position and speed estimation in ultra-high-speed (UHS) permanent magnet synchronous motors (PMSM) without position sensors, multiple refinements to the traditional extended electromotive force (EEMF) estimation algorithm are proposed in this paper. The key improvements include discretization compensation, high-frequency harmonic filtering, and the real-time adjustment of the phase-locked loop (PLL) bandwidth. Firstly, a discrete model is introduced to address EMF cross-coupling issues. Secondly, an improved proportional resonant (IPR) controller eliminating static errors is utilized in place of the conventional proportional-integral (PI) controller and low-pass filter (LPF) to enable precise electromotive force extraction, effectively filtering high-frequency harmonics that arise in low carrier ratio conditions. Based on a standard PR design, the IPR controller offers a streamlined calculation for target leading angles in delay compensation schemes to effectively mitigate discretization and delay errors. Additionally, an adaptive phase-locked loop (AQPLL) dynamically adjusts its bandwidth during acceleration to balance noise rejection and phase delay, reducing position estimation errors and optimizing torque. Simulations and experimental analyses on a motor (90,000 rpm, 30 kW) validate the effectiveness of the proposed sensorless driving techniques and demonstrate enhanced performance in position and velocity estimation, compared to the conventional EEMF approach. Full article
(This article belongs to the Section Physical Sensors)
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<p>Overview of DSP-based sensorless FOC strategy.</p>
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<p>Overview of the conventional sensorless control strategy.</p>
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<p>Conventional EEMF observer.</p>
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<p>EMF cross-coupling decoupling during A/D conversion.</p>
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<p>Conventional QPLL structure.</p>
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<p>Bode diagram of conventional PR controller.</p>
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<p>(<b>a</b>) Block diagrams of continuous-domain resonant controllers featuring dual integrators, denoted as <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mrow> <mi>P</mi> <msub> <mi>R</mi> <mi>h</mi> </msub> </mrow> </msub> <mrow> <mo stretchy="false">(</mo> <mi>s</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math>. (<b>b</b>) Resonant controllers with a phase lead <math display="inline"><semantics> <mrow> <msubsup> <mi>G</mi> <mrow> <mi>P</mi> <msub> <mi>R</mi> <mi>h</mi> </msub> </mrow> <mi>d</mi> </msubsup> <mrow> <mo stretchy="false">(</mo> <mi>s</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math>. (<b>c</b>) Digital resonant controllers with corrected poles <math display="inline"><semantics> <mrow> <msubsup> <mi>G</mi> <mrow> <mi>P</mi> <msub> <mi>R</mi> <mi>h</mi> </msub> </mrow> <mi>d</mi> </msubsup> <mrow> <mo stretchy="false">(</mo> <mi>z</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math>.</p>
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<p>Rapid motor speed variations resulting from a low QPLL bandwidth lead to misalignment in the control frames.</p>
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<p>Block diagram of the adaptive QPLL.</p>
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<p>Pole-zero map of AQPLL with fixed damping factor and increased natural frequency.</p>
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<p>Overview of the optimization sensorless control strategy.</p>
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<p>Experimental setup for sensorless FOC of the high-speed PMSM (<b>a</b>) Motor and compressor load. (<b>b</b>) Motor controller.</p>
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<p>Comparison of experimental results corresponding to the time when the motor speed increases from 50,000 rpm to 90,000 rpm within 0.6 s: (<b>a</b>) traditional EEMF, (<b>b</b>) IPR-based EEMF, and (<b>c</b>) adding AQPLL.</p>
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<p>When the motor is running stably, a 3Nm load is added within 0.25s, and the corresponding change in speed and position is reached when the load reaches stable operation again: (<b>a</b>) traditional EEMF, (<b>b</b>) IPR-based EEMF, and (<b>c</b>) adding AQPLL.</p>
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<p>Three methods of position error when the motor reaches steady-state operation at 90,000 rpm.</p>
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<p>Speed and position estimation errors when the motor parameters change to 2R and 0.8L under the EEMF method.</p>
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<p>Comparison of harmonic frequencies of current and EMF signals before and after IPR.</p>
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