ZCL-TCYB3227389

Citation Author(s):
Chen-Liang
Zhang
Northeastern University
Submitted by:
Chen-Liang Zhang
Last updated:
Sun, 01/01/2023 - 10:50
DOI:
10.21227/js96-v170
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Abstract 

This paper investigates the prescribed performance control (PPC) problem for a class of nonlinear strict-feedback systems with sensor/actuator faults. A shifting function is introduced to modify the output tracking error generated by the practically measured system state, based on which an improved PPC method is proposed to achieve the convergence of output tracking error to the prescribed region, and this convergence is shown to be independent of the initial tracking condition and insusceptible to sensor/actuator faults. The faults-induced uncertainties together with the nonlinear dynamics are compensated by involving a radial basis function neural network (RBFNN) to make the controller robust adaptive fault-tolerant without prior knowledge of fault coefficients. Via Lyapunov stability analysis, it is proven that all signals in the closed-loop system are semi-globally uniformly ultimately bounded. The effectiveness and superiority of the method are demonstrated by two simulation examples.

Instructions: 

This is an paper abstract with  paper doi: 10.1109/TCYB.2022.3227389

Funding Agency: 
the National Natural Science Foundation of China
Grant Number: 
62173079;U1808205

Comments

good

Submitted by Getamesay Bedada on Fri, 03/31/2023 - 10:41