PD-type Parameter Optimization Iterative Learning Control Algorithm Based on Inverse Model

2019 2nd International Conference on Advanced Materials, Intelligent Manufacturing and Automation

Xiong, Zhanlei^1 ; Zeng, Qingshan^1 ; Yin, Mingjun^1

School of Electrical Engineering, Zhengzhou University, Zhengzhou

450001, China^1

关键词: Differentiation parameters;    Iterative learning control;    Iterative learning control algorithm;    Learning gain matrix;    Linear time-invariant system;    Parameter optimization;    Single input and single outputs;    Tracking control problem;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/569/5/052001/pdf
DOI  :  10.1088/1757-899X/569/5/052001

来源: IOP

【 摘 要 】

In this paper, a proportion-differentiation parameter optimization iterative learning control (POILC) algorithm based on inverse model (IM) is proposed for the tracking control problem of a class of single input and single output discrete linear time-invariant (LTI) systems. The algorithm establishes the parameter optimal performance index function and adds the learning gain matrix to proportion and differential terms of the control law, which enable the algorithm to be applied to non-positive definite systems and to converge monotonously and rapidly. The purpose of above method is to reduce the influence of model accuracy on tracking performance. Compared with previous algorithms, the proposed algorithm has been improved to a certain extent in tracking accuracy, convergence speed and robustness.