Adaptive neural network state constrained fault-tolerant control for a class of pure-feedback systems with actuator faults

In this paper, an adaptive neural network (NN) constrained fault-tolerant control (FTC) method is proposed for a class of nonlinear pure feedback systems with actuator faults and state constraints. By designing a fault-tolerant compensation controller and a new asymmetric time-varying tangent Barrie...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2022-06, Vol.490, p.431-440
Main Authors: Ma, Lei, Wang, Zhanshan, Wang, Changlai
Format: Article
Language:English
Subjects:
Fault-tolerant control
Neural network
Pure-feedback Systems
State constraint
Tangent Barrier Lyapunov function
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Summary:In this paper, an adaptive neural network (NN) constrained fault-tolerant control (FTC) method is proposed for a class of nonlinear pure feedback systems with actuator faults and state constraints. By designing a fault-tolerant compensation controller and a new asymmetric time-varying tangent Barrier Lyapunov function (ATVTBLF), the problem of time-varying asymmetric state constraints caused by actuator failure is solved. The actuator takes into account both loss of effectiveness and bias fault. Different from the existing asymmetric BLF, the new BLF solves the problem of asymmetry under tangent constraints and expands the application range of the asymmetric constraint. NN is used to approximate the unknown items generated in the process of designing fault-tolerant controllers. Based on the Lyapunov stability analysis, it is proved that all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), and all states do not violate the bounds. Finally, the simulation example verifies the effectiveness of the proposed method.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2021.12.017