Robust adaptive FTC allocation for over-actuated systems with uncertainties and unknown actuator non-linearity

• Published in: IET control theory & applications Vol. 12; no. 2; pp. 273 - 281
• Main Authors: Zhi, Jianhui, Chen, Yong, Dong, Xinmin, Liu, Zongcheng, Shi, Chao
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 30.01.2018
• Subjects: actuators; adaptive control; approximation error elimination; approximation theory; closed loop systems; closed‐loop system; control nonlinearities; control system synthesis; fault tolerant control; fault‐tolerant capability; FTC problem; individual actuators; linear term; Lyapunov methods; Lyapunov stability theorem; matched disturbance; neurocontrollers; nonlinear control systems; nonlinear term; over‐actuated systems; radial basis function networks; radial basis function neural network; RBFNN; Research Article; robust adaptive fault‐tolerant control allocation method; robust adaptive FTC allocation method; robust control; robust term; stability; uncertain systems; unknown actuator nonlinearity; unmodelled dynamics; virtual control law design; weighted pseudo‐inverse control allocation; approximation error elimination; radial basis function neural network; weighted pseudo-inverse control allocation; fault-tolerant capability; FTC problem; virtual control law design; linear term; individual actuators; nonlinear term; actuators; neurocontrollers; control nonlinearities; radial basis function networks; matched disturbance; robust term; robust control; stability; unknown actuator nonlinearity; approximation theory; uncertain systems; over-actuated systems; robust adaptive FTC allocation method; adaptive control; control system synthesis; Lyapunov stability theorem; closed loop systems; RBFNN; nonlinear control systems; unmodelled dynamics; closed-loop system; fault tolerant control; Lyapunov methods; robust adaptive fault-tolerant control allocation method
• DOI: 10.1049/iet-cta.2017.0385
• ISSN: 1751-8644

In this study, a robust adaptive fault-tolerant control (FTC) allocation method is proposed to address the FTC problem of over-actuated systems in the presence of matched disturbance, unmodelled dynamics and unknown actuator non-linearity simultaneously. The main idea is to design the virtual control law via robust adaptive control and then distribute the virtual control among individual actuators by weighted pseudo-inverse control allocation whether the actuators are faulty or not. The virtual control consists of three terms: a linear term is designed through adaptive control to maintain stability, a non-linear term constructed by radial basis function neural network (RBFNN) is used to approximate the unmodelled dynamics, and a robust term is added to eliminate the approximation error introduced by RBFNN as well as matched disturbance and unknown actuator non-linearity. With the aid of the Lyapunov stability theorem, the convergence of the closed-loop system is proven. The simulation results demonstrate the effectiveness, fault-tolerant capability and robustness of the proposed method.