The Research And Practice Upon Predictive Function Decoupling Control Based On The Particle Swarm Optimization

This thesis focus on the research of predictive function decoupling control based on the particle swarm optimization. And this control method is used in the temperature control of the barrel of the injection molding.Multivariable coupling phenomenon is everywhere in the actual industrial production. In order to control systems accurately, predictive function decoupling control algorithm is studied from the point of view of decoupling control in this thesis. The decoupling control problem of coupled multi-variable system is simplified into the predictive function decoupling control of some single-variable systems. And the overall optimization strategy is changed to decentralized optimization strategy. Because of the feature of the predictive functional control algorithm, the basic functions are introduced. Then the design freedom is increased and the work of calculation is reduced. So the design of parameters and the process of solving the algorithm are deeply simplified. An analysis decoupling control volume can be received by the algorithm. The calculation of equations and their control parameters all can be calculated off the line. What is more, the algorithm is simple and it is easy to realize the complex, high-dimensional variable decoupling control system. The simulation experiments show that predictive functional decoupling control method has good decoupling effect.Predictive function decoupling control, however, often face the problem of prediction model mismatch. To solve that, the identifying predictive model based on particle swarm method is proposed. Particle swarm optimization algorithm is used in this thesis, and the system parameter space is studied qualitatively. And the problem of how to identify the system is changed into optimization of the parameter space. The local optimum in the process of optimization can be avoided with the algorithm. Instead, parallel search will be realized in the whole parameter space to find the optimal solution of the system parameter. The simulation experiments show that particle swarm optimization algorithm is better than the genetic optimization algorithm in the multivariable system model identification. Because of the identification of multi-variable system model, the Particle Swarm Optimization (PSO) algorithm is better than genetic algorithm. So in this paper, we take particle swarm prediction model to improve the predictive functional control method for decoupling. The PSO can enhance the accuracy of the prediction model and improve the predictive function of the decoupling control stability. The simulation experiments show that the improved forecast function decoupling method-based on PSO is better than the predictive functional decoupling control method, and the former can achieve good control effect.This predictive function based on particle swarm decoupling control method is applied to the actual cylinder temperature control experiment. firstly, using particle swarm intelligence methods in the identification of barrel heating section, and the mathematical model of the cylinder heating section can be identified; and then the application of predictive functional based on PSO control methods to develop indicators,and the optimize method can achieve decoupling. The results of simulation show that comparing with the original temperature of the injection molding machine cylinder integral PID controller, the forecast function based on particle swarm decoupling control method can achieve better control performance and robustness.