Robust Model Predictive Control for Nonlinear Systems With Incremental Control Input Constraints

This paper presents a robust model predictive control (RMPC) algorithm for nonlinear discrete-time systems subject to bounded disturbances and incremental control input constraints. To guarantee recursive feasibility, a terminal inequality constraint is integrated into the proposed RMPC algorithm. By employing constraint tightening techniques, we derive an upper bound on admissible disturbances that ensures the input-to-state stability (ISS) for the closed-loop system. The effectiveness of the proposed algorithm is validated through numerical simulations and practical experiments involving the control of a four-wheel mobile robot. The results demonstrate the capability of the proposed method to maintain system stability and optimize control performance in the presence of external disturbances.