Decentralized adaptive neural prescribed performance control for high-order stochastic switched nonlinear interconnected systems with unknown system dynamics. (January 2019)

• Record Type: Journal Article
• Authors: Si, Wenjie; Dong, Xunde; Yang, Feifei
• Is Part Of: ISA transactions. Volume 84(2019)
• Page Start: 55
• Page End: 68
• Publication Date: 2019-01
• Subjects: Switched systems -- Decentralized adaptive control -- High-order stochastic nonlinear systems -- Common Lyapunov function -- Prescribed performance control (PPC); Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2018.09.019
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: In this paper, the problem of decentralized adaptive neural backstepping control is investigated for high-order stochastic nonlinear systems with unknown interconnected nonlinearity and prescribed performance under arbitrary switchings. For the control of high-order nonlinear interconnected systems, it is assumed that unknown system dynamics and arbitrary switching signals are unknown. First, by utilizing the prescribed performance control (PPC), the prescribed tracking control performance can be ensured, while the requirement for the initial error is removed. Second, at each recursive step, only one adaptive parameter is constructed to overcome the over-parameterization, and RBF neural networks are employed to tackle the difficulties caused by completely unknown system dynamics. At last, based on the common Lyapunov stability method, the decentralized adaptive neural control method is proposed, which decreases the number of learning parameters. It is shown that the designed common controller can ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), and the prescribed tracking control performance is guaranteed under arbitrary switchings. The simulation results are presented to further illustrate the effectiveness of the proposed control scheme. Highlights: For nonlinear interconnected systems, arbitrary switching signals are unknown. The stability of high-order systems under arbitrary switching is investigated. A desired tracking performance is achieved with uncertain interactions. This paper estimates the norm of NN weight vectors, reducing learning parameters.