Command filter-based fixed-time fault estimation and compensation control for nonlinear systems with prescribed performance

For the uncertain nonlinear systems with prescribed performance, the command filter-based fixed-time fault estimation and compensation control strategy is investigated in this study. The radial basis function neural networks (RBFNNs) are utilized to approximate the uncertain nonlinear terms. Simultaneously, the composite disturbance observer is established to quickly estimate external disturbances, approximation errors, and additive actuation fault. Moreover, the actuation effectiveness of the actuator is quickly estimated online by constructing the cubic absolute-value Lyapunov function. Therefore, based on the fast estimation of the actuator fault parameters, the fixed-time fault-tolerant control method is proposed by adopting the command filter backstepping technology and prescribed performance function, which can compensate for the adverse effect of actuator fault and keep the tracking error stable in a short time interval. Finally, a simulation example is given to prove the performance of the designed controller.