Research On Predictive Control Method Based On LM-NMEA Algorithm And ELM-RBF Neural Network

With the advent of the new technology era represented by 5G technology and artificial intelligence AI,all aspects of society will usher in major changes,and the traditional control industry is also facing an impact.The principle of inverted pendulum is applied to various control industries,but in the traditional 4G mode,excessively high delay causes the pendulum to stabilize for a long time,and with the application of ultra-low latency 5G technology,the pendulum The steady state time will be reduced by an order of magnitude.With the improvement of technical content,the complexity of various controlled systems is getting higher and higher,but the control effects of traditional control methods such as PID control are becoming more and more inadequate.Since the 1970 s,a kind of predictive control theory based on the identification of the model of the controlled object in the industrial process control,which uses computer rapid calculation as a means,has shown strong industrial support with new technologies such as 5G in the new era.This paper studies and analyzes a predictive control method based on neural network,and analyzes and discusses the background and significance,basic principles and ideas and current status of predictive control.The details are as followsThis article first combs the development background and current situation of predictive control,expounds the basic theory of predictive control,and introduces several popular predictive control methods.For the controlled object of nonlinear system,this paper uses neural network model predictive control method that is based on ELM-RBF neural network to identify the controlled object and build a predictive model,and at the same time use Levenberg-Marquardt method in the predictive control rolling optimization link to target constraints Optimize solution.In order to make up for the defect that the initial value of ELM-RBF neural network is difficult to determine and the LM algorithm is too dependent on the starting point,this paper uses an improved thinking evolution algorithm,NMEA,to optimize it separately.The NMEA algorithm is obtained by improving the MEA algorithm through niche technology and drift clustering algorithm.Finally,this paper uses a typical nonlinear system,the continuous stirred reactor(CSTR)as the controlled object,uses the NMEA-RLM-RBFNN to identify the prediction model,and the LM-NEMA algorithm is used as the objective function solution algorithm in the rolling optimization stage.The predictive control method controls the output concentration of reactants in the CSTR system.A CSTR model is built on the Matlab/Simulink simulation platform,and simulation experiments are designed to verify the control effect of the predictive control method.Experiments show that the neural network model predictive control method used in this paper shows a good control effect on the output control of reactant concentration in the CSTR system.