Enhanced P-Type Control: Indirect Adaptive Learning From Set-Point Updates

In this article, an indirect adaptive iterative learning control (iAILC) scheme is proposed for both linear and nonlinear systems to enhance the P-type controller by learning from set points. An adaptive mechanism is included in the iAILC method to regulate the learning gain using input-output measu...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on automatic control 2023-03, Vol.68 (3), p.1600-1613
Main Authors: Chi, Ronghu, Li, Huaying, Shen, Dong, Hou, Zhongsheng, Huang, Biao
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive iterative learning control
Adaptive systems
Algorithms
Analytical models
Convergence
convergence analysis
Data models
Iterative methods
Learning
Linear systems
Nonlinear systems
Nonlinearity
P-type controller
set-point updating method
System theory
Systems theory
Tuning
Uncertainty
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this article, an indirect adaptive iterative learning control (iAILC) scheme is proposed for both linear and nonlinear systems to enhance the P-type controller by learning from set points. An adaptive mechanism is included in the iAILC method to regulate the learning gain using input-output measurements in real time. An iAILC method is first designed for linear systems to improve control performance by fully utilizing model information if such a linear model is known exactly. Then, an iterative dynamic linearization (IDL)-based iAILC is proposed for a nonlinear nonaffine system, whose model is completely unknown. The IDL technique is employed to deal with the strong nonlinearity and nonaffine structure of the systems such that a linear data model can be attained consequently for the algorithm design and performance analysis. The convergence of the developed iAILC schemes is proved rigorously, where contraction mapping, two-dimensional (2-D) Roesser's system theory, and mathematical induction are employed as the basic analysis tools. Simulation studies are provided to verify the developed theoretical results.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2022.3154347