Adaptive decentralized prescribed performance control for a class of large-scale nonlinear systems subject to nonsymmetric input saturations

• Published in: Neural computing & applications Vol. 34; no. 13; pp. 11123 - 11140
• Main Authors: Zhu, Shan-Liang, Han, Yu-Qun
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2022; Springer Nature B.V
• Subjects: Adaptive control; Algorithms; Approximation; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Control algorithms; Control methods; Control theory; Controllers; Data Mining and Knowledge Discovery; Design; Image Processing and Computer Vision; Nonlinear systems; Original Article; Probability and Statistics in Computer Science; Recursive methods; Saturation; Tracking control; Tracking errors; Multi-dimensional Taylor network; Input saturation; Prescribed performance; Adaptive control; Large-scale nonlinear systems
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-022-07032-8

This paper investigates an adaptive decentralized predefined performance control problem for a class of large-scale nonlinear systems with nonsymmetric input saturation by using multi-dimensional taylor network (MTN) approach. Firstly, the input saturation model is approximated by a smooth function with a bounded approximation error and unknown nonlinear functions are estimated by MTNs. Secondly, a decentralized tracking control algorithm is established by integrating the idea of prescribed performance control into backstepping recursive technique. Thirdly, by using the designed MTN-based adaptive decentralized controller, all the closed-loop signals are bounded and all the tracking errors satisfy the predefined transient and steady-state performance, respectively. Finally, the presented control method is effective by introducing three examples, and the simulation results verify that the correctness and reasonableness of the proposed control algorithm.