Zone control with Model Predictive Control for much constrained states process

In industrial process, especially in chemical process control, controlled process has the characteristic of many intermediate state variables. The problem of how to handle the amplitude constraints of input, output and some important state variables, which commonly exists in process control, is the nodus of current researches. Model Predictive Control (MPC) can bring the constraints of input, output variables into the optimization problem, which is solved on-line to obtain the control law. With this advantage MPC is superior to other control methods to control constrained system. However, the disposal of amplitude constraint of important state variables in current MPC algorithms, usually consider it as one part of constraints of output variables, increases the number of controlled variables. A new zone predictive control algorithm is presented in this paper to deal with the constraint of important state variables This new algorithm transforms constraint of important state variables to matrix inequality form of input increment on-line, and brings it into the optimization problem for obtaining the control law. In the end, simulation results of control problem about side-stream quality of crude tower show the effectiveness and feasibleness of the proposed algorithm.