L1 Adaptive Fault-Tolerant Attitude Tracking Control of UAV Systems Subject to Faults and Input Saturation

This paper investigates an adaptive fault-tolerant control scheme for an unmanned aerial vehicle (UAV) attitude control system in the presence of multiple faults, input saturation, and external disturbances. The attitude control system of a UAV is divided into an outer loop consisting of attitude angles and an inner loop consisting of attitude angle velocities. The nonlinear dynamic inversion (NDI) method based on sliding mode surface is used to construct the outer loop controller to ensure fast response and accurate attitude tracking. The treatment of the inner loop with multiple faults, input saturation, and external disturbances is more complex. First, input saturation and sensor faults are converted successively for ease of analysis. After the conversion and reorganization of such uncertainties including actuator faults, sensor faults, input saturation, and external disturbances, a nonlinear uncertain system model is developed. Second, a L1 neural network adaptive fault-tolerant controller is designed to deal with uncertainties, where radial basis function neural networks (RBFNNs) are applied to approximate the nonlinear function in the system model. The L1 adaptive fault-tolerant controller includes three parts: state predictor, adaptive law, and control law. Adaptive laws based on the projection operator estimate and update the unknown parameters in the system and transfer them to the control law and the state predictor to compensate for the effects of uncertainties. The control law with a low-pass filter also eliminates the high-frequency oscillations caused by a high adaptive gain in conventional adaptive algorithms, which guarantee the robustness and fast self-adaptation. Finally, simulation results are given to verify the effectiveness and feasibility of the proposed controller.