Chance Constrained Model Predictive Control Based on Set-Valued Optimal Algorithm

This paper adopts one kind of classical predictive control, namely model algorithmic control, to control the distillation process. Due to finite cognition degree for the distillation system and existence noise in the chemical industry, we present a chance constrained model predictive control algorithm to eliminate the effect of parameter pertubation and noise. In view of the linear impulse response model, we introduce set-valued optimal algorithm and orthogonal standardization method to transform the chance constrained model predictive control to a general model predictive control problem. For the determinate MPC, efficient quadratic programming exsits to solve such problem. Applying such controller to a distillation system, output constraint condition will be satisfied with a predefined probability.

Constrained Model Algorithmic Control for the Generalized Hammerstein Model with Impulse Response

A Generalized Hammerstein Model with Impulse Response for symmetric nonlinear systems was presented in this paper. A hyper-quadratic object function was developed by adding highest order control input term with a symbolic function into the object function, and a constrained multi-step model algorithmic control for the non-minimum phase systems with open-loop stable characterization was established by forcing the control input with saturated limitation. The algorithm with one control policy can guarantee the simulative results without steady state deviation and the control input being converged to a varying region centered in the zero-point. Simulated results validated the constrained model algorithmic control for Generalized Hammerstein Model with impulse response is reasonable and applicable.