Reference Governor for Input-Constrained MPC to Enforce State Constraints at Lower Computational Cost

In this paper, a control scheme is developed based on an input constrained Model Predictive Control (MPC) law and the idea, usual of Reference Governors (RG), of modifying the reference command to enforce constraints. The proposed scheme, referred to as the RGMPC, can handle (possibly nonlinear) state and input constraints and only requires optimization for MPC with polytopic input constraints for which fast algorithms exist. Conditions are given that ensure recursive feasibility of the RGMPC scheme and finite-time convergence of the modified reference command to the desired reference command. Simulation results for a spacecraft rendezvous maneuver with linear and nonlinear constraints demonstrate that the RGMPC scheme has lower average computational time than state and input constrained MPC with similar performance.