Neural networks-based adaptive fault-tolerant control for a class of nonstrict-feedback nonlinear systems with actuator faults and input delay

Mohamed Kharrat; Hadil Alhazmi; Mohamed Kharrat; Hadil Alhazmi

doi:10.3934/math.2024668

AIMS Mathematics

2024, Volume 9, Issue 6: 13689-13711. doi: 10.3934/math.2024668

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Research article

Neural networks-based adaptive fault-tolerant control for a class of nonstrict-feedback nonlinear systems with actuator faults and input delay

Mohamed Kharrat ¹,
Hadil Alhazmi ^{2
,
,}

1.
Department of Mathematics, College of Science, Jouf University, P.O. Box 2014, Sakaka, Saudi Arabia
2.
Department of Mathematical Sciences, College of Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

Received: 25 January 2024 Revised: 10 March 2024 Accepted: 02 April 2024 Published: 15 April 2024
MSC : 92B20, 93C10, 93C40

This paper addresses the challenge of adaptive control for nonstrict-feedback nonlinear systems that involve input delay, actuator faults, and external disturbance. To deal with the complexities arising from input delay and unknown functions, we have incorporated Pade approximation and radial basis function neural networks, respectively. An adaptive controller has been developed by utilizing the Lyapunov stability theorem and the backstepping approach. The suggested method guarantees that the tracking error converges to a compact neighborhood that contains the origin and that every signal in the closed-loop system is semi-globally uniformly ultimately bounded. To demonstrate the efficacy of the proposed method, an electromechanical system application example, and a numerical example are provided. Additionally, comparative analysis was conducted between the Pade approximation proposed in this paper and the auxiliary systems in the existing method. Furthermore, error assessment criteria have been employed to substantiate the effectiveness of the proposed method by comparing it with existing results.
- backstepping method,
- actuator faults,
- nonlinear systems,
- input delay,
- electromechanical system
Citation: Mohamed Kharrat, Hadil Alhazmi. Neural networks-based adaptive fault-tolerant control for a class of nonstrict-feedback nonlinear systems with actuator faults and input delay[J]. AIMS Mathematics, 2024, 9(6): 13689-13711. doi: 10.3934/math.2024668

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Abstract

This paper addresses the challenge of adaptive control for nonstrict-feedback nonlinear systems that involve input delay, actuator faults, and external disturbance. To deal with the complexities arising from input delay and unknown functions, we have incorporated Pade approximation and radial basis function neural networks, respectively. An adaptive controller has been developed by utilizing the Lyapunov stability theorem and the backstepping approach. The suggested method guarantees that the tracking error converges to a compact neighborhood that contains the origin and that every signal in the closed-loop system is semi-globally uniformly ultimately bounded. To demonstrate the efficacy of the proposed method, an electromechanical system application example, and a numerical example are provided. Additionally, comparative analysis was conducted between the Pade approximation proposed in this paper and the auxiliary systems in the existing method. Furthermore, error assessment criteria have been employed to substantiate the effectiveness of the proposed method by comparing it with existing results.

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