A reinforcement discrete neuro-adaptive control for unknown piezoelectric actuator systems with dominant hysteresis

[[abstract]]The theoretical and experimental studies of a reinforcement discrete neuro-adaptive control for unknown piezoelectric actuator systems with dominant hysteresis are presented. Two separate nonlinear gains, together with an unknown linear dynamical system, construct the nonlinear model (NM) of the piezoelectric actuator systems. A nonlinear inverse control (NIC) according to the learned NM is then designed to compensate the hysteretic phenomenon and to track the reference input without the risk of discontinuous response. Because the uncertainties are dynamic, a recurrent neural network (RNN) with residue compensation is employed to model them in a compact subset. Then, a discrete neuro-adaptive sliding-mode control (DNASMC) is designed to enhance the system performance. The stability of the overall system is verified by Lyapunov stability theory. Comparative experiments for various control schemes are also given to confirm the validity of the proposed control.[[sponsorship]]IEEE Computational Intelligence Society[[notice]]補正完畢[[incitationindex]]SC

On the adaptive controls of nonlinear systems with different hysteresis model representations

The hysteresis phenomenon occurs in diverse disciplines ranging from physics to biology, from material science to mechanics, and from electronics to economics. When the hysteresis nonlinearity precedes a controlled system, the nonlinearity usually causes the overall closed-loop system to exhibit inaccuracies or oscillations, even leading to instability. Control techniques to mitigate the unwanted effects of hysteresis have been studied for decades and have recently once again attracted significant attention. In this thesis, several adaptive control strategies are developed for systems with different hysteresis model representations to guarantee the basic stability requirement of the closed-loop systems and to track a desired trajectory with a certain precision. These proposed strategies to mitigate the effects of hysteresis are as follows: i). With the classical Duhem model, an observer-based adaptive control scheme for a piezoelectric actuator system is proposed. Due to the unavailability of the hysteresis output, an observer-based adaptive controller incorporating a pre-inversion neural network compensator is developed for the purpose of mitigating the hysteretic effects; ii). With the Prandtl-Ishlinskii model, an adaptive tracking control approach is developed for a class of nonlinear systems in p-normal form by using the technique of adding a power integrator to address the challenge of how to fuse this hysteresis model with the control techniques to mitigate hysteresis, without necessarily constructing a hysteresis inverse; iii). With a newly proposed hysteresis model using play-like operators, two control strategies are proposed for a class of nonlinear systems: one with sliding mode control and the other with backstepping technique