Varying model based adaptive predictive control of highly nonlinear chemical process

In chemical processes there commonly exist highly nonlinear processes such as polymerization, PH process. Traditional multiple model control only establishes linearized submodels an finite steady states, and the loss of linearized models on nonsteady states can't meet the requirement for rigorous models during transition. So this paper proposes a varying model based adaptive predictive control algorithm to solve the above problems effectivelly -- a nonlinear state space model is linearized on each nonsteady operating state every step, the acquired linearized submodel is applied to state feedback predictive control, and the linearized submodel and controller parameters both automatically adapt to the actual nonlinear process according to the move of operating state. Through the simulation of PH control, compared with traditional multiple model control, it shows its better effect on highly nonlinear processes. Finally several problems of this method are analyzed and discussed and the concerned future research aspects are proposed.