scholarly journals Research On Adaptive Optimal Iterative Learning Control Based on Least Squares

2021 ◽  
Author(s):  
Liang-Liang Yang ◽  
Xiang Luo ◽  
Rui Yuan ◽  
Hui Zhang
Keyword(s):  
Least Squares ◽  
Iterative Learning Control ◽  
Servo System ◽  
Learning Control ◽  
Iterative Learning ◽  
Tracking Performance ◽  
System Model ◽  
Time Varying ◽  
Parameter Perturbation ◽  
System A

Abstract Traditional Optimal Iterative Learning Control (TOILC) can effectively improve the tracking performance of the servo system. However, there may be parameter perturbation in the running process of the servo system, and its parameters are constantly changing slowly. As a result, the convergence of TOILC becomes worse, and the tracking performance of the system deteriorates seriously. Therefore, in view of the time-varying characteristics of the system, a least squares optimal iterative learning control (LSAOILC) algorithm is proposed. In the process of iteration, the nominal model of the system is identified according to the input and output signals so as to update the optimal iterative learning controller, which does not need to obtain the exact system model information in advance, making up for the shortage of TOILC. The simulations and experiments prove the effectiveness of the proposed strategy for the servo system.

Fuzzy Iterative Learning Control of Servo System

2011 ◽  
Vol 217-218 ◽  
pp. 917-923
Author(s):  
Hong Li Liu ◽  
Qi Xin Zhu ◽  
Xiang Wen Chen
Keyword(s):  
Fuzzy Control ◽  
Iterative Learning Control ◽  
Fuzzy Controller ◽  
Design Method ◽  
Servo System ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Control Performance ◽  
System A

According to the repeatability characteristics of servo system, a fuzzy iterative learning controller is proposed which combining advantages of fuzzy control and iterative learning control. Fuzzy control has better robustness, and does not require accurate model of the system, only need the previous experience, the design method is simple. Fuzzy controller is used in the position-loop, but the static error is difficult to eliminate, iterative learning controller use the control error to adjust the previous control input to reduce the error of next time. The combination of these two intelligent control algorithms can improve the control performance of servo system effectively. Simulation results show that the fuzzy control combined with iterative learning control can achieve better control performance in servo system.

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