Global Asymptotic Attitude Tracking for Uncertain Spacecraft With Full-State Error Constraints

This article studies the global asymptotic neural network (NN) tracking problem for full-state error constrained spacecraft attitude systems with actuator faults, inertia uncertainties, and external disturbances. In the literature, most existing NN control schemes can only achieve semiglobally bounded stability since the approximation capability of NNs is confined to a compact domain called the approximation domain. Differently, an attitude tracking control strategy in conjunction with a modified smooth switching mechanism is proposed to ensure the global asymptotic stability. Specifically, an adaptive NN controller is developed within the approximation domain to address unknown nonlinearities, and a robust controller is activated outside the approximation domain to drive back the system states. With the proposed design, both attitude and angular velocity errors (collectively defined as the full-state errors) are rigorously proven to globally asymptotically converge to zero. Moreover, the full-state errors are preserved within the unified prescribed performance constraints, which are uniform with respect to any initial conditions, thereby eliminating the requirement for offline computation of the performance boundary. In addition, the undesirable feasibility conditions on virtual control laws are completely eliminated. Theoretical analysis and numerical simulations validate the effectiveness of the proposed method.