Adaptive Nonsingular Fixed-Time Prescribed Performance Control for Nonlinear Systems Subject to Unknown Actuator Faults

The challenge of adaptive fixed-time trajectory tracking control for nonlinear systems subject to actuator faults and unknown nonlinearities is addressed within this work. First, radial basis function neural networks (RBF NNs) are applied to approximate completely unknown nonlinearities. Second, an adaptive approach is employed to handle the challenge of unknown actuator failure coefficients, thereby releasing the need for a priori knowledge of the actuator failure coefficients. Moreover, unlike the typical prescribed performance function, an improved version is introduced, ensuring that the tracking error reaches and remains within the specified tolerance before a predefined time. Relying on Lyapunov stability theory and backstepping, an adaptive fixed-time fault-tolerant control strategy is recursively designed by establishing a continuous switching function, effectively overcoming the singularity issue arising from the derivation of virtual inputs. Furthermore, the proposed control scheme demonstrates the system’s stability and predefined tracking performance, and simulation experiments prove its effectiveness.