Robust Model Predictive Control for Attitude Control Tracking

In this chapter, we study the optimal time-varying attitude control problem for rigid spacecraft with unknown system constraints and additive perturbations. A dual-loop cascaded tracking control framework is established by designing a new nonlinear tube-based robust model predictive control (TRMPC) algorithm. The proposed TRMPC algorithm explicitly considers the effect of disturbances and applies tightened system constraints to predict the motion of the nominal system. The computed optimal control is combined with a nonlinear feedback method so that the actual system trajectory can always be controlled in a tubular region around the nominal solution. To promote the recursive feasibility of the optimization process and to ensure the input-state stability tracking control process, a terminal controller and corresponding terminal invariant set are also constructed. The effectiveness of the proposed two-loop TRMPC control scheme for reference trajectory tracking problem is verified through an experimental study. Several comparative studies are performed and the results obtained show that the proposed scheme is more promising for constraint handling and attitude tracking than other recently developed schemes considered in this study.