Neural Network-Based Adaptive Fixed-Time Fault-Tolerant Control for Robotic Manipulators

This paper proposes a neural-network-based adaptive fixed-time fault-tolerant control (AFTFTC) scheme for robotic manipulators with unknown dynamics and actuator faults. A radial basis function neural network (RBFNN) is first employed to approximate the unknown nonlinear dynamics, an adaptive law is designed to estimate an unknown scalar parameter associated with the norm of the ideal neural-network weights. To enhance the fault-tolerance capability, an additional adaptive law is developed to compensate for unknown actuator faults. By integrating these adaptive mechanisms into the backstepping control framework, the proposed AFTFTC controller is constructed to handle both normal and faulty operating conditions. Theoretical analysis shows that both the joint tracking errors and the adaptive parameter estimation errors converge to a small neighborhood of zero within a fixed time, independent of the initial conditions. Comparative experiments on a robotic manipulator are carried out to verify the improved tracking accuracy, robustness, and fault-tolerant performance of the proposed method.