A framework of iterative learning control under random data dropouts: Mean square and almost sure convergence

2017 ◽  
Vol 31 (12) ◽  
pp. 1825-1852 ◽  
Author(s):  
Dong Shen ◽  
Jian-Xin Xu
Keyword(s):  
Iterative Learning Control ◽  
Almost Sure Convergence ◽  
Learning Control ◽  
Iterative Learning ◽  
Mean Square ◽  
Random Data ◽  
Data Dropouts

A Technical Overview of Recent Progresses on Stochastic Iterative Learning Control

2018 ◽  
Vol 06 (03) ◽  
pp. 147-164 ◽  
Author(s):  
Dong Shen
Keyword(s):  
Stochastic Approximation ◽  
Kalman Filtering ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Random Data ◽  
Research Directions ◽  
Stochastic Noises ◽  
Analysis Methods ◽  
Data Dropouts

This paper contributes to a technical overview of recent progresses on stochastic iterative learning control (ILC), where stochastic ILC implies the learning control for systems with various random signals and factors such as stochastic noises, random data dropouts and inherent random asynchronism. The fundamental principles of ILC are first briefed with emphasis on the system formulations and typical analysis methods. Then the recent progresses on stochastic ILC are reviewed in three parts: additive randomness case, multiplicative randomness case, and coupled randomness case, respectively. Three major approaches, i.e., expectation-based method, Kalman filtering-based method, and stochastic approximation-based method, are clarified. Promising research directions are also presented for further investigation.

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