Adaptive Neural Backstepping Control for Uncertain MIMO Systems With Sensor/Actuator Faults and Asymmetric Time-Interval Output Constraints

This article deals with a class of strict-feedback multi-input–multi-output (MIMO) nonlinear systems, which are characterized by model uncertainties, unknown control coefficient matrices, nonvanishing external disturbances, and multiple unknown time-varying faults in both sensors and actuators. To achieve accurate reference trajectory tracking while fulfilling fixed time-interval asymmetric output constraints (FTAOCs), a novel filter-backstepping adaptive-neural-fault-tolerant constrained (FBAFC) algorithm is developed based on only partial information from faulty sensors. The controller integrates radial basis function neural networks (RBFNNs) to approximate unknown nonlinearities and compensate for compound uncertainties. A coordinate transformation is introduced for iterative backstepping design, and a new barrier Lyapunov function (BLF) with a shift function is derived in three phases. The convergence of tracking errors is guaranteed theoretically, even in the presence of multiple actuator faults. Finally, simulation results are presented in two scenarios to validate the proposed method.