Neural Networks-Based Adaptive Finite-Time Fault-Tolerant Control for a Class of Strict-Feedback Switched Nonlinear Systems

2019 ◽  
Vol 49 (7) ◽  
pp. 2536-2545 ◽  
Author(s):  
Lei Liu ◽  
Yan-Jun Liu ◽  
Shaocheng Tong
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Finite Time ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Switched Nonlinear Systems ◽  
Strict Feedback

scholarly journals Neural networks-based adaptive finite-time control of switched nonlinear systems under time-varying actuator failures

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xikui Liu ◽  
Xiurong Shi ◽  
Yan Li
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Finite Time ◽  
Fault Tolerant ◽  
Tracking Error ◽  
Adaptive Controller ◽  
Time Varying ◽  
Switched Nonlinear Systems ◽  
Actuator Failures ◽  
Time Control

AbstractThis paper is dedicated to neural networks-based adaptive finite-time control design of switched nonlinear systems in the time-varying domain. More specifically, by employing the approximation ability of neural networks system, an integrated adaptive controller is constructed. The main aim is to make sure the closed-loop system in arbitrary switching signals is semi-global practical finite-time stable (SGPFS). A backstepping design with a common Lyapunov function is proposed. Unlike some existing control schemes with actuator failures, the key is dealing with the time-varying fault-tolerant job for the switched system. It is also proved that all signals in the system are bounded and the tracking error can converge in a small field of the origin in finite time. A practical example is presented to illustrate the validity of the theory.

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