Neural network-based adaptive fault-tolerant control for strict-feedback nonlinear systems with input dead zone and saturation

IF 3.7 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2025-01-01 DOI:10.1016/j.jfranklin.2024.107471
Mohamed Kharrat , Moez Krichen , Hadil Alhazmi , Paolo Mercorelli
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Abstract

This study investigates the issue of adaptive fault-tolerant neural control in strict-feedback nonlinear systems. The system is subjected to actuator faults, dead-zone and saturation. To model the unknown functions, radial basis function neural networks (RBFNN) are employed. The proposed approach utilizes a backstepping technique to formulate an adaptive fault-tolerant controller, drawing upon the Lyapunov stability theory and the approximation capabilities of RBFNN. The resultant controller guarantees the boundedness of all signals in the closed-loop system, ensuring precise tracking of the reference signal by the system output with a small, bounded error. Finally, simulation results are provided to illustrate the efficacy of the proposed strategy in addressing actuator faults, dead-zone, and saturation.
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来源期刊
CiteScore
7.30
自引率
14.60%
发文量
586
审稿时长
6.9 months
期刊介绍: The Journal of The Franklin Institute has an established reputation for publishing high-quality papers in the field of engineering and applied mathematics. Its current focus is on control systems, complex networks and dynamic systems, signal processing and communications and their applications. All submitted papers are peer-reviewed. The Journal will publish original research papers and research review papers of substance. Papers and special focus issues are judged upon possible lasting value, which has been and continues to be the strength of the Journal of The Franklin Institute.
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