Nothing Special   »   [go: up one dir, main page]

Xie et al., 2009 - Google Patents

Observer based control of piezoelectric actuators with classical Duhem modeled hysteresis

Xie et al., 2009

View PDF
Document ID
12306695479646608215
Author
Xie W
Fu J
Yao H
Su C
Publication year
Publication venue
2009 American Control Conference

External Links

Snippet

An observer-based adaptive control scheme for piezoelectric actuators is proposed. In the developed method the classical Duhem model is adopted to describe the hysteresis exhibited in the piezoelectric actuators. Due to the unavailability of the hysteresis output, an …
Continue reading at folk.ntnu.no (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B11/00—Automatic controllers
    • G05B11/01—Automatic controllers electric
    • G05B11/32—Automatic controllers electric with inputs from more than one sensing element; with outputs to more than one correcting element
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric

Similar Documents

Publication Publication Date Title
Yao et al. Adaptive RISE control of hydraulic systems with multilayer neural-networks
Boukattaya et al. Adaptive nonsingular fast terminal sliding-mode control for the tracking problem of uncertain dynamical systems
Al-Dhaifallah et al. Fuzzy Fractional‐Order PID Controller for Fractional Model of Pneumatic Pressure System
Ge et al. Adaptive neural network control for a class of MIMO nonlinear systems with disturbances in discrete-time
Xie et al. Nonlinear modeling and decoupling control of XY micropositioning stages with piezoelectric actuators
Liu et al. A new control scheme for nonlinear systems with disturbances
Boulkroune et al. Indirect adaptive fuzzy control scheme based on observer for nonlinear systems: A novel SPR-filter approach
Feliu et al. Passivity-based control of single-link flexible manipulators using a linear strain feedback
Wang et al. Rapid-convergent nonlinear differentiator
Zhang et al. PID-like coupling control of underactuated overhead cranes with input constraints
Xie et al. Neural network‐based adaptive control of piezoelectric actuators with unknown hysteresis
Chu et al. Backstepping control for flexible joint with friction using wavelet neural networks and L2‐gain approach
Vu et al. Sliding variable-based online adaptive reinforcement learning of uncertain/disturbed nonlinear mechanical systems
Mahjoub et al. Second-order sliding mode control applied to inverted pendulum
Xie et al. Observer based control of piezoelectric actuators with classical Duhem modeled hysteresis
Munoz-Vazquez et al. High-gain fractional disturbance observer control of uncertain dynamical systems
Li et al. Adaptive inverse control of non-linear systems with unknown complex hysteretic non-linearities
Zhao et al. Neural network-based adaptive controller design for robotic manipulator subject to varying loads and unknown dead-zone
Liang et al. Task space trajectory tracking control of robot manipulators with uncertain kinematics and dynamics
Wang et al. Output Feedback Fractional‐Order Nonsingular Terminal Sliding Mode Control of Underwater Remotely Operated Vehicles
Wang et al. On the control of plants with hysteresis: overview and a Prandtl-Ishlinskii hysteresis based control approach
Leeghim et al. Adaptive nonlinear control using input normalized neural networks
Mou et al. Active disturbance rejection sliding mode control for robot manipulation
Genno et al. An Adaptive Neuro-Fuzzy Controller for Vibration Suppression of Flexible Structuress
Yao et al. Neural network based adaptive control of piezoelectric actuator with unknown hysteresis