Fixed-time neural adaptive fault-tolerant control for space manipulator under output constraints

This article investigates the challenging problem for the fixed-time trajectory tracking control of free-flying space manipulator under output constraints and actuator faults. A novel fixed-time neural adaptive fault-tolerant control approach is proposed by incorporating the barrier Lyapunov function (BLF) and neural network (NN) into the backstepping control design. The BLF is introduced to preserve the position tracking errors always within the predefined output constraints. Moreover, the NN approximation is adopted to compensate for the lumped uncertain term in the feedforward loop. Lyapunov analysis shows the practical fixed-time stability of the resulting closed-loop system. The proposed controller can guarantee the position and velocity tracking errors converge to the small neighborhoods about the origin in fixed time even in the presence of output constraints and actuator faults and thus ensuring safety. Lastly, the effectiveness and excellent tracking performance of the proposed control approach are illustrated through simulations and comparisons.