Compensation of hysteresis in piezoelectric actuator with iterative learning control

Abstract

This paper presents the application of iterative learning control (ILC) to compensate hysteresis in a piezoelectric actuator. The proposed controller is a hybrid of proportional-integral-differential (PID) control, whose main function is for trajectory tracking, and a chatter-based ILC, whose main function is for hysteresis compensation. Stability analysis of the proposed ILC is presented, with the PID included in the dynamic of the piezoelectric actuator. The performance of the proposed controller is analysed through simulation and verified with experiment with a piezoelectric actuator.

Stable Adaptive Fuzzy Control with Hysteresis Observer for Three-Axis Micro/Nano Motion Stages

“…Let (6) where 1 is a virtual input. Define the tracking error signal as ν (7) where d is the desired trajectory for the three-axis motion. Differentiating Equation 7, we have…”

“…In order to linearize the control system, many researches focused on the inverse feedforward compensation based on some inverse hysteresis model. Several models have been suggested for describing the complex hysteretic behavior, for example, the Preisach model in Ge and Jouaneh [4,5], Yu et al [6], and Liu et al [7], the generalized Preisach model in Ge and Jouaneh [8], the dynamic Preisach model in Yu et al [9]; the generalized Maxwell elasto-slip model in Goldfarb and Celanovic [10]; the variable time-relay hysteresis model in Tsai and Chen [11]; the Prandtl-Ishlinskii (PI) model (a subclass of the Preisach model) in Ang. et al [1] and Hassani and Tjahjowidodo [12]; the Duhem model in Stepanenko and Su [13]; the polynomial approximation method in Croft and Devasia [14]; and the Jiles-Atherton model in Dupre et al [15].…”

Current-Cycle Iterative Learning Control for High-Precision Position Tracking of Piezoelectric Actuator System via Active Disturbance Rejection Control for Hysteresis Compensation

“…The idea of using ILC to compensate for tracking errors in PEA has been implemented in many existing works. In [19], experiments have been implemented to verify that using ILC to eliminate the repetitive tracking error is effective. In [20], an ILC based model-inverse method is explored for PEA systems without considering the hysteresis nonlinearity, and the controller design is simplified by linearizing the hysteresis.…”

Optimal fuzzy iterative learning control based on artificial bee colony for vibration control of piezoelectric smart structures

“…After one critical report published by Arimoto in English [3], IL control had a significant progress in both theory and application [4,5]. In many cases, it is essential to apply this algorithm to find the system inputs that make the system outputs close possible to the desired outputs, such as hysteresis compensation in a piezoelectric actuator [6], achievement of the extreme precision motion tracking for the control system [7], state estimation on repetitive process systems [5], point-to-point motion control of robotic arm [8]. However, few papers can be found about the applications of IL control for active vibration control of piezoelectric smart structures.…”