Application Studies On Model Predictive Control Of Complex Industrial Process

Model predictive control is an advanced computer optimized control algorithm, which is based on the three basic principles of the prediction model, rolling optimization and feedback correction. With the industrial process control requirements continue to increase, the traditional PID controller has been difficult to meet the requirements of complex industrial process control, model predictive control due to low accuracy requirement model, online calculation convenient, high control efficiency, to be very successful in the actual complex industrial process applications. In this paper, our research is divided into two aspects of the theory and practical application, the major theoretical research is state space model predictive control algorithm and multi-model predictive control algorithm; application research is to use model predictive control algorithm control the thermal field temperature of single crystal furnace crystal pulling process.This paper studies on state space model predictive control algorithm and multi-model predictive control algorithm. Give model predictive controller parameter adjustment strategy, and through the simulation prove the correctness of the parameter adjustment strategy.This paper discusses the basic principle of the single crystal furnace control system and crystal pulling process, analyze the widespread problem of the domestic single crystal furnace control system and crystal pulling process. Temperature is the core factors of crystal growth, control the thermal field temperature of single crystal furnace is a hot research topic, this paper proposed using state space model predictive control algorithm to control the thermal field temperature of equal diameter stage, using the subspace identification algorithm to obtain the temperature model. Simulate equal diameter stage thermal field temperature control, compare the control performance of constrained optimization model predictive controller with traditional PID controller, the simulation results proved that for the single crystal furnace equal diameter stage temperature control object, the model predictive controller designed in this paper is better than traditional PID controller.Crystal furnace crystal pulling process is a complex nonlinear system, us only a single model of model predictive controller to control the thermal field temperature of all crystal pulling process cannot achieve satisfactory control. Multi-model predictive control can control complex system well that operating conditions change, using the subspace identification algorithm to obtain the crystal furnace crystal pulling process temperature model at every stage, the corresponding controllers were designed for each model, the entire control role is a weighted combination of each controller output and weighted algorithm using the improved Bayesian probability weighted algorithm. So this paper proposed using improved Bayesian probability weighted multi-model predictive control algorithm to control the thermal field temperature of all crystal pulling process. The simulation results proved that the Bayesian probability weighted multi-model predictive control method have good quality control.