Neural-network-based adaptive fault-tolerant vibration control of single-link flexible manipulator

This paper proposes an adaptive fault-tolerant control scheme for a single-link flexible manipulator with actuator failure and uncertain boundary disturbance. The dynamic model of the flexible manipulator as-described by partial differential equations (PDEs) is derived under Hamilton's principle. The dynamic model is then used to design an adaptive fault-tolerant control (FTC) scheme which tracks the given angle and regulates vibration in the case of actuator failure. The boundary disturbance is compensated by a radial basis function (RBF) neural network. The whole closed-loop system is proven asymptotically stable by Lyapunov direct method and LaSalle's invariance principle. Simulation results indicate that the proposed controller is superior to the traditional PD controller.