Neural network-based finite-time adaptive tracking control of nonstrict-feedback nonlinear systems with actuator failures

2021 ◽  
Vol 545 ◽  
pp. 298-311
Author(s):  
Guozeng Cui ◽  
Wei Yang ◽  
Jinpeng Yu
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Finite Time ◽  
Tracking Control ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking ◽  
Actuator Failures

scholarly journals Finite-Time Adaptive Tracking Control for a Class of Pure-Feedback Nonlinear Systems with Disturbances via Decoupling Technique

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Qiangqiang Zhu ◽  
Ben Niu ◽  
Shengtao Li ◽  
Peiyong Duan ◽  
Dong Yang
Keyword(s):  
Nonlinear Systems ◽  
Finite Time ◽  
Tracking Control ◽  
Design Procedure ◽  
Adaptive Controller ◽  
Closed Loop System ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking ◽  
Feedback Structure ◽  
Derivatives Of

This paper addresses the finite-time adaptive tracking control problem for a class of pure feedback nonlinear systems whose nonaffine functions may not be differentiable. By properly modeling the nonaffine function, the design difficulty of the pure feedback structure is overcome without using the median value theorem. In our design procedure, an finite-time adaptive controller is elaborately developed using the decoupling technology, which eliminates the limitation assumption on the partial derivatives of nonaffine functions. Furthermore, the constructed controller can stabilize the system within a finite-time so that all signals in the closed-loop system are semiglobally uniformly finite-time bounded (SGUFB), while ensuring the tracking performance. Finally, the simulation results prove the effectiveness of the proposed method.

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