Model predictive control algorithm for nonlinear chemical processes

This paper considers MPC algorithm for nonlinear plants using successive linearization. The prediction equation was computed via nonlinear integration. Local linear approximation of the state equation was used to develop an optimal prediction equation for the future states. The output prediction was made linear with respect to the undecided control input moves, which allowed to reduce the MPC optimization problem to a quadratic programming problem. The effectiveness of the proposed MPC algorithm is demonstrated on two nonlinear industrial examples.