Combining Kernel Partial Least-Squares Modeling and Iterative Learning Control for the Batch-to-Batch Optimization of Constrained Nonlinear Processes

2010 ◽  
Vol 49 (16) ◽  
pp. 7470-7477 ◽  
Author(s):  
Yingwei Zhang ◽  
Yunpeng Fan ◽  
Pengchao Zhang
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Nonlinear Processes ◽  
Kernel Partial Least Squares

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.

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