A Technical Overview of Recent Progresses on Stochastic Iterative Learning Control

Abstract

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.

Improving Boundary Level Calculation in Quantized Iterative Learning Control With Encoding and Decoding Mechanism

“…Due to the fact that the data in the entire time series can be obtained from the previous iteration, this non-causal learning algorithm can be realized in practical applications Here, we analyze the characteristics of the encoding and decoding mechanism. By substituting the formulation of s k from (6) into the estimate of the system outputŷ k (t) in 7, we obtain…”

“…After more than three decades of development, ILC has become one of the great branches of intelligent control with rigorous mathematical description. Presently, ILC has made great progress in learning algorithms, convergence, robustness, learning speed and engineering application research [1]- [6].…”

Adaptive learning control for general nonlinear systems with nonuniform trial lengths, initial state deviation, and unknown control direction

“…Over the past three decades, ILC has made great progress. [1][2][3][4][5][6] In addition, due to its simple but effective structure, in a practical environment, permanent magnet step motors, 7 high-speed rail train, 8 and robotic-assisted biomedical system 9 use ILC to resolve the corresponding questions.…”

“…Because ILC only needs little system information to design the controller, it can be regard as a typical data‐driven strategy. Over the past three decades, ILC has made great progress . In addition, due to its simple but effective structure, in a practical environment, permanent magnet step motors, high‐speed rail train, and robotic‐assisted biomedical system use ILC to resolve the corresponding questions.…”

A Novel Iterative Learning Control Approach Based on Steady-State Kalman Filtering

“…From recent surveys [14], [15], it is observed that an overwhelming majority of ILC algorithms have employed on-line real-time learning in fixing the problems of actual engineering. Theoretically, it is natural to study convergence and stability of robust ILC, adaptive ILC, and stochastic ILC.…”