Predictive Iterative Learning Speed Control With On-Line Identification for Ultrasonic Motor

Shi Jingzhuo,Wenwen Huang

doi:10.1109/access.2020.2989866

Abstract

Aiming at the needs of ultrasonic motor motion control, a new two-dimensional (2D) predictive control objective function is proposed. Different from the existing methods, the objective function consists of three terms, including the product of the control quantity and the error of the previous control process. Based on the objective function, the predictive iterative learning control (ILC) law is derived by using the design method of generalized predictive control (GPC) without specifying ILC law form in advance. An on-line identification method for model parameters is given to realize effective identification under a small amount of data circumstances, and therefore, the parameters of controller are adjusted adaptively according to the identification results. The proposed control method is validated both in simulation and experiment. The experimental results show that the proposed predictive iterative learning control strategy can obtain better control effect than GPC, and has more obvious characteristics of iterative learning control. It can maintain the expected performance under the condition of intermittent loading and replacing the motor. It presents strong robustness.

Highlights

As a new type of actuator, ultrasonic motor has obvious nonlinear characteristics, and it is not easy to obtain good control performance [1], [2]
PREDICTIVE ITERATIVE LEARNING CONTROL SCHEME The controlled auto-regressive integrated moving average (CARIMA) process model is used by generalized predictive control (GPC) for controller design [16], [17]
DESIGN OF PREDICTIVE ITERATIVE LEARNING SPEED CONTROLLER FOR ULTRASONIC MOTOR the proposed predictive iterative learning control (ILC) control strategy is applied to the speed control of ultrasonic motor, and its effectiveness is verified by simulation and experiment

Summary

INTRODUCTION

As a new type of actuator, ultrasonic motor has obvious nonlinear characteristics, and it is not easy to obtain good control performance [1], [2]. In [12], a model-free trajectory tracking of multiple-input multiple-output (MIMO) systems is proposed by the combination of ILC and primitives It guarantees that the optimal performance with respect to a new trajectory to be tracked is theoretically predicted, without executing the trajectory and without learning by repetition. Huang: Predictive Iterative Learning Speed Control With On-Line Identification for Ultrasonic Motor convergence along the cycle index and the control stability along the time index, it can be consider that combining ILC with the feedback control method along the time index. Based on the traditional GPC objective function, a product term of the control quantity and the error of the previous control process is added to the objective function It attempts to integrate the generalized predictive control methods such as multi-step predictive and rolling optimization into the ILC law. In the case of a new motor with different characteristics from the original motor, the control response can still maintain the desired performance

PREDICTIVE ITERATIVE LEARNING CONTROL SCHEME

CONCLUSION