Adaptive fault‐tolerant robust control based on radial basis function neural network for a class of mechanical systems with input constraints. (21st February 2022)

• Record Type: Journal Article
• Authors: Gao, Shiqi; Liu, Jinkun
• Is Part Of: International journal of robust and nonlinear control. Volume 32:Number 7(2022)
• Page Start: 4099
• Page End: 4112
• Publication Date: 2022-02-21
• Subjects: fault‐tolerant control -- hyperbolic tangent function -- input constraint -- loss of effectiveness fault -- RBF neural network -- robust adaptive control; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.6075
• Languages: English
• ISSNs: 1049-8923

Abstract

Abstract: In this paper, a robust adaptive fault‐tolerant control (FTC) strategy based on hyperbolic tangent function is proposed for a class of mechanical systems with unknown disturbance and unknown nonlinear friction model. The robust adaptive FTC algorithm proposed in this paper can maintain the system performance even after partial failure occurs in the actuator. Different from the traditional fault‐tolerant control algorithms, the proposed algorithm uses the hyperbolic tangent function and a projection algorithm to limit the control input within a known upper bound. In addition, radial basis function neural network is used to approximate the unknown continuous nonlinear function that contains the friction model in the mechanical system. The control target of the system is realized; and the asymptotic stability of the closed‐loop system is proved via the Lyapunov direct method. Finally, the numerical simulation results also demonstrate the effectiveness of the designed controller.