Adaptive fault-tolerant control for stratospheric airships with full-state constraints, input saturation, and external disturbances

This study addresses the trajectory tracking problem of a stratospheric airship subject to actuator faults, full-state constraints, input saturation, and unknown external disturbances. Improved airship state-space equations are obtained by simultaneously considering actuator faults, input saturation, and external disturbances. An adaptive fault-tolerant controller with a neural network (AFTCNN) was proposed with backstepping as the main framework. A tan-type barrier Lyapunov function (TBLF) and auxiliary systems are combined to address the full-state constraints and input saturation. Adaptive fault-tolerant and radial basis function neural network (RBFNN) control laws are proposed to estimate the actuator faults, and to approximate the disturbances, respectively. Stability analysis indicates that the tracking error converges to a neighborhood of zero; the error estimation of actuator faults and disturbances is bounded, and the state constraints are never violated. The standard backstepping method with and without RBFNN, and an adaptive fault-tolerant controller with and without RBFNN are compared in the numerical simulation. The results verify that the proposed method is superior to other methods in terms of trajectory-tracking performance and estimation of actuator faults and external disturbances, with smaller forces and torques to track the trajectory.