Adaptive decentralized fixed‐time neural control for constrained strong interconnected nonlinear systems with input quantization

• Published in: International journal of robust and nonlinear control Vol. 34; no. 14; pp. 9899 - 9927
• Main Authors: Wei, Fansen, Zhang, Liang, Niu, Ben, Zong, Guangdeng
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 25.09.2024
• Subjects: Adaptive systems; Constraints; fixed‐time stability; full‐state constraints; Gaussian process; Input quantization; Neural networks; Nonlinear control; nonlinear state‐dependent function; Nonlinear systems; Tracking control; Tracking errors; unknown strong interconnections
• ISSN: 1049-8923; 1099-1239
• DOI: 10.1002/rnc.7497

This article investigates the problem of adaptive decentralized fixed‐time tracking control for strong interconnected nonlinear systems with full‐state constraints and input quantization. During the control design process, the assumption that the strong interconnection terms are bounded is removed via an inherent feature of the Gaussian function in neural networks. Unlike presvious nonlinear state‐dependent function (NSDF) that can only handle a single constraint, a novel form of NSDF is introduced to cope with more types of state constraints in this article. Meanwhile, the introduced NSDF is still available when the system states are unconstrained. Simultaneously, quantized input is directly handled by utilizing the intrinsic characteristics of the hysteresis quantizer. Then, based on the Lyapunov stability theory, all signals in the closed‐loop systems and tracking error are guaranteed to be bounded within fixed‐time. Finally, the feasibility of the proposed control scheme is illustrated by simulation results.