Iterative learning control algorithm for sensor fault nonlinear systems

Issue title: Meta-Heuristic Techniques for Solving Computational Engineering Problems: Challenges and New Research Directions

Guest editors: Suresh Chandra Satapathy, Rashmi Agrawal and Vicente García Díaz

Article type: Research Article

Authors: Lan, Zimian^{; *}

Affiliations: School of Mechanical and Electrical Engineering, Hechi University, Yizhou, Guangxi, China

Correspondence: [*] Corresponding author. Zimian Lan, E-mail: [email protected].

Abstract: In this paper, we propose a new iterative learning control algorithm for sensor faults in nonlinear systems. The algorithm does not depend on the initial value of the system and is combined with the open-loop D-type iterative learning law. We design a period that shortens as the number of iterations increases. During this period, the controller corrects the state deviation, so that the system tracking error converges to the boundary unrelated to the initial state error, which is determined only by the system’s uncertainty and interference. Furthermore, based on the λ norm theory, the appropriate control gain is selected to suppress the tracking error caused by the sensor fault, and the uniform convergence of the control algorithm and the boundedness of the error are proved. The simulation results of the speed control of the injection molding machine system verify the effectiveness of the algorithm.

Keywords: Nonlinear system, iterative learning control, sensor fault, initial state error, λ norm

DOI: 10.3233/JIFS-189432

Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 5927-5934, 2021