Regular Papers

International Journal of Control, Automation, and Systems 2023; 21(9): 2810-2820

https://doi.org/10.1007/s12555-021-1107-8

© The International Journal of Control, Automation, and Systems

Adaptive ILC Design for Nonlinear Discrete-time Systems With Randomly Varying Trail Lengths and Uncertain Control Directions

Qing-Yuan Xu, Yun-Shan Wei, Jing Cheng, and Kai Wan*

Huizhou University

Abstract

In this paper, an adaptive iterative learning control (ILC) design method is proposed for a class of nonlinear discrete-time systems with nonaffine structure, randomly varying trail length, and uncertain control direction. In order to achieve repetitive tracking control of the nonaffine structure systems with uncertain control direction, randomly varying trail length, and other uncertainties, we apply a high-order neural network to approximate the expected system input. Then, a novel adaptation law is designed for the neural network weight vector. The main feature of the method proposed in this paper is that the weight vector norm instead of the weight vector itself is updated iteratively to realize the successive approximation of the expected system input, the custom-designed identification mechanism is not necessary to deal with the uncertain control direction, and the analysis of randomly varying trail lengths problem is strictly established. The convergence of the proposed adaptive ILC is set up by a composite energy function. The effectiveness of the proposed adaptive ILC design is validated by two simulation examples.

Keywords Adaptive iterative learning control (ILC), high-order neural network, nonlinear discrete-time systems, randomly varying trail lengths, uncertain control directions.

Article

Regular Papers

International Journal of Control, Automation, and Systems 2023; 21(9): 2810-2820

Published online September 1, 2023 https://doi.org/10.1007/s12555-021-1107-8

Copyright © The International Journal of Control, Automation, and Systems.

Adaptive ILC Design for Nonlinear Discrete-time Systems With Randomly Varying Trail Lengths and Uncertain Control Directions

Qing-Yuan Xu, Yun-Shan Wei, Jing Cheng, and Kai Wan*

Huizhou University

Abstract

In this paper, an adaptive iterative learning control (ILC) design method is proposed for a class of nonlinear discrete-time systems with nonaffine structure, randomly varying trail length, and uncertain control direction. In order to achieve repetitive tracking control of the nonaffine structure systems with uncertain control direction, randomly varying trail length, and other uncertainties, we apply a high-order neural network to approximate the expected system input. Then, a novel adaptation law is designed for the neural network weight vector. The main feature of the method proposed in this paper is that the weight vector norm instead of the weight vector itself is updated iteratively to realize the successive approximation of the expected system input, the custom-designed identification mechanism is not necessary to deal with the uncertain control direction, and the analysis of randomly varying trail lengths problem is strictly established. The convergence of the proposed adaptive ILC is set up by a composite energy function. The effectiveness of the proposed adaptive ILC design is validated by two simulation examples.

Keywords: Adaptive iterative learning control (ILC), high-order neural network, nonlinear discrete-time systems, randomly varying trail lengths, uncertain control directions.

IJCAS
June 2024

Vol. 22, No. 6, pp. 1761~2054

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