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

TL;DR

This paper introduces RGMPC, a control scheme combining reference governors with input-constrained MPC, reducing computational cost while ensuring constraint satisfaction and convergence in complex control scenarios.

Contribution

The paper presents a novel RGMPC scheme that enforces state constraints at lower computational cost using reference modification and fast MPC algorithms.

Findings

Lower average computational time compared to traditional constrained MPC.

Ensures recursive feasibility and finite-time convergence.

Effective in nonlinear and linear constraint scenarios.

Abstract

In this paper, a control scheme is developed based on an input constrained Model Predictive Controller (MPC) and the idea of modifying the reference command to enforce constraints, usual of Reference Governors (RG). The proposed scheme, referred to as the RGMPC, requires optimization for MPC with input constraints for which fast algorithms exist, and can handle (possibly nonlinear) state and input constraints. Conditions are given that ensure recursive feasibility of the RGMPC scheme and finite-time convergence of the modified command to the 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 as compared to state and input constrained MPC with similar performance.