Optimizing Prediction Dynamics With Saturated Inputs for Robust Model Predictive Control

A model predictive control algorithm based on offline optimization of prediction dynamics enables an efficient online computation. However, the price for this efficiency is a reduction in the degree of optimality. This article presents a new method for overcoming this weakness, yielding a significant improvement in the degree of optimality, and achieving this with no increase in an online computational load. Two numerical examples with comparison to earlier solutions from the literature illustrate the effectiveness of the proposed algorithm.