Finite-time adaptive neural control and almost disturbance decoupling for disturbed MIMO non-strict-feedback nonlinear systems. Issue 16 (November 2020)

• Record Type: Journal Article
• Authors: Jiang, Kun; Wang, Xiaomei; Niu, Ben; Wang, Zhenhua; Li, Junqing; Duan, Peiyong; Yang, Dong
• Is Part Of: Journal of the Franklin Institute. Volume 357:Issue 16(2020)
• Page Start: 11750
• Page End: 11772
• Publication Date: 2020-11
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2019.09.042
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: This paper investigates the finite-time adaptive neural control and almost disturbance decoupling problems for multi-input/multi-output (MIMO) nonlinear systems with disturbances and non-strict-feedback structure. In the design procedure of the adaptive controller, neural networks are employed to estimate the unknown nonlinearities and Young's inequality is utilized to cope with the disturbance terms derived from all subsystems. In order to characterize the disturbance attenuation performance of finite-time adaptive control, a criterion named finite-time almost disturbance decoupling is first developed. Under this criterion, an adaptive neural controller is designed via the backstepping method and the appropriate selection of Lyapunov function. It is revealed that the proposed controller can guarantee all variables of the closed-loop system are bounded, and the performance of finite-time almost disturbance decoupling is realized. Finally, a practical example is employed to validate the effectiveness of the designed controller.