Feasible Real-time Nonlinear Model Predictive Control

Abstract

This paper discusses an algorithm for efficiently calculating the control moves for constrained nonlinear model predictive control. The approach focuses on real-time optimization strategies that maintain feasibility with respect to the model and constraints at each iteration, yielding a stable technique suitable for suboptimal model predictive control of nonlinear process. We present a simulation to illustrate the performance of our method.