Adaptive Prescribed-Time Tracking Control of Spacecraft with Deferred Full-State Constraints

This paper investigates the prescribed-time tracking control problem for spacecraft with parametric uncertainties and deferred full-state constraints. Such kinds of constraints universally take effect some time after the system maneuvers, instead of being fulfilled initially, and are regularly encountered in practical applications. To achieve the deferred constraints, the modified states are incorporated into the control system with the aid of the state shifting function, thus removing the feasibility condition required by the traditional barrier Lyapunov functions. Considering that most of the existing results are only able to achieve tracking control in an asymptotic or finite-time manner, in this paper a prescribed-time control strategy for a spacecraft tracking control system is therefore studied. Synthesized with deferred constraints, the settling time can be roughly specified in a predetermined time bucket. Moreover, the parametric uncertainties can be addressed with a neural-based minimum-learning-parameter (MLP) algorithm. Finally, simulation results are provided to illustrate the efficacy of the designed control strategy.