Optimized Data-Driven Prescribed Performance Attitude Control for Actuator Saturated Spacecraft

This article addresses the crucial requirements in spacecraft attitude control: prescribed performance guarantees under actuator saturation and real-time cost optimization. As an application-oriented study, an approximate optimal prescribed performance attitude control scheme is proposed for this objective. To be specific, the prescribed performance constraint is converted into the system dynamics and merged into the adaptive dynamic programming design philosophy. Subsequently, the online learning law is designed based on a special saturated Hamilton–Jacobi–Bellman error, in which a dynamical scale is introduced to adjust the learning gain by measured data. It enhances learning efficiency and applicability. Then, uniformly ultimately bounded stability of the whole system is achieved with guaranteed convergence of optimization by the Lyapunov-based stability analysis. Finally, both numerical simulation and hardware-in-the-loop experiments demonstrate the superiority and effectiveness of the proposed method. These attributes and outcomes attained will promote the development of practical space missions.