Open Access
ARTICLE
Improved High Order Model-Free Adaptive Iterative Learning Control with Disturbance Compensation and Enhanced Convergence
Zhiguo Wang*, Fangqing Gao, Fei Liu
Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation,
Jiangnan University, Wuxi, 214122, China
* Corresponding Author: Zhiguo Wang. Email:
(This article belongs to the Special Issue: Advances on Modeling and State Estimation for Industrial Processes)
Computer Modeling in Engineering & Sciences 2023, 134(1), 343-355. https://doi.org/10.32604/cmes.2022.020569
Received 01 December 2021; Accepted 22 February 2022; Issue published 24 August 2022
Abstract
In this paper, an improved high-order model-free adaptive iterative control (IHOMFAILC) method for a class of
nonlinear discrete-time systems is proposed based on the compact format dynamic linearization method. This
method adds the differential of tracking error in the criteria function to compensate for the effect of the random
disturbance. Meanwhile, a high-order estimation algorithm is used to estimate the value of pseudo partial derivative
(PPD), that is, the current value of PPD is updated by that of previous iterations. Thus the rapid convergence of the
maximum tracking error is not limited by the initial value of PPD. The convergence of the maximum tracking error
is deduced in detail. This method can track the desired output with enhanced convergence and improved tracking
performance. Two examples are used to verify the convergence and effectiveness of the proposed method.
Keywords
Cite This Article
APA Style
Wang, Z., Gao, F., Liu, F. (2023). Improved high order model-free adaptive iterative learning control with disturbance compensation and enhanced convergence. Computer Modeling in Engineering & Sciences, 134(1), 343-355. https://doi.org/10.32604/cmes.2022.020569
Vancouver Style
Wang Z, Gao F, Liu F. Improved high order model-free adaptive iterative learning control with disturbance compensation and enhanced convergence. Comput Model Eng Sci. 2023;134(1):343-355 https://doi.org/10.32604/cmes.2022.020569
IEEE Style
Z. Wang, F. Gao, and F. Liu "Improved High Order Model-Free Adaptive Iterative Learning Control with Disturbance Compensation and Enhanced Convergence," Comput. Model. Eng. Sci., vol. 134, no. 1, pp. 343-355. 2023. https://doi.org/10.32604/cmes.2022.020569