Chance constrained nonlinear model predictive control

Abstract

A novel robust controller, chance constrained nonlinear MPC, is presented. Time-dependent uncertain variables are considered and described with piecewise stochastic variables over the prediction horizon. Restrictions are satisfied with a user-defined probability level. To compute the probability and its derivatives of satisfying process restrictions, the inverse mapping approach is extended to dynamic chance constrained optimization cases. A step of probability maximization is used to address the feasibility problem. A mixing process with both an uncertain inflow rate and an uncertain feed concentration is investigated to demonstrate the effectiveness of the proposed control strategy. © 2007 Springer-Verlag Berlin Heidelberg.