Model Reference Adaptive Iterative Learning Speed Control for Ultrasonic Motor

Shi Jingzhuo,Wenwen Huang

doi:10.1109/access.2020.3029106

Abstract

In the design process of the controller, the adaptive gain of model reference adaptive control (MRAC) often requires a tradeoff between the adaptive ability, robustness and stability of the control system. The tradeoff of adaptive gain leads to poor control performance and increase design difficulty. Aiming at this problem, the iterative learning idea is introduced into the model reference adaptive control strategy. The control parameter adaptive law based on the parameters of the previous control process is designed. For scalar systems, a new control strategy is constructed, which is the combination of MRAC and iterative learning control (ILC). The adaptive ability of the model reference adaptive controller is improved by using learning ability of ILC. An appropriate composite energy function is designed to prove the uniform convergence of the proposed control strategy and the boundedness of the control quantity. The proposed control strategy is applied to the ultrasonic motor. The effectiveness of the proposed control strategy is verified by experiments and simulations. The controller is designed by using the first-order model that is large different from the actual object. It verifies that the control strategy has strong robustness to model deviation and online time-varying characteristics.

Highlights

Since the model reference adaptive control (MRAC) strategy was proposed, it has been widely used in various practical control systems due to its adaptive ability to changing controlled objects and the stability guaranteed by the design process based on the Lyapunov function [1]–[6]
The results show that the proposed model reference adaptive iterative learning control (MRAILC) strategy is effective
In MRAC, the tradeoff of adaptive gain leads to poor control performance and increase design difficulty

Summary

INTRODUCTION

Since the model reference adaptive control (MRAC) strategy was proposed, it has been widely used in various practical control systems due to its adaptive ability to changing controlled objects and the stability guaranteed by the design process based on the Lyapunov function [1]–[6]. It is necessary to select a sufficiently small adaptive gain value to sacrifice system control performance to maintain stability and the necessary degree of robustness In this case, the adaptive ability of MRAC strategy is weakened. Huang: Model Reference Adaptive Iterative Learning Speed Control for Ultrasonic Motor that under the same MRAC design conditions without the knowledge of the plant parameters, an MRAC system ensures that the tracking error has the stronger higher order convergence property Such a new MRAC system property leads to several new results of adaptive stabilization and tracking control using either state feedback or output feedback.

MODEL REFERENCE ADAPTIVE ITERATIVE LEARNING CONTROL ALGORITHM

CONCLUSION