A computationally efficient nonlinear MPC algorithm

In this paper, a novel model predictive control (MPC) algorithm for control of nonlinear multivariable systems is proposed. The online computational demand of the algorithm depends only on the number of manipulated variables; it does not depend on the input (or control) horizon. Thus, the online computational demand is significantly smaller than conventional nonlinear model predictive control algorithms which attempt to solve the online optimization problems exactly. We show that asymptotic stability can be guaranteed in some cases. Its feasibility for practical implementation is demonstrated on a distillation column dual composition control problem using a rigorous tray-by-tray model (with input horizon of 10)