Forward Neural Networks Control Theoretical Research And Its Application

There are lots of non-linear systems in the nature.Because classical and morden control methods have common limitation:mathematical model of the plant has to be known in advance.But in fact many plants are intricate uncentaintied and time-varying.In addition,they also are non-linear.Though there are methods of system identification in control theories,the identification theories and ways of non-linear and time-varying system are not mature and systematic.It is very difficult to carry out effective real-time control.The neural networks can approximate random non-linear relations and study by itself ,and it provides new thoughts and new ways for solving these problems.This paper focuses on the theories and controller designs of forward neural netwoks—BP network.At first, the structure and algorithms of BP network are deeply researched,the ralations between momentum factor and convergence speed,convergence accuracy are revealed and a kind of improved BP algorithm is presented.Then the identification method based on BP network with adaptive learning rate is studied and the simulaton indicates it can adaptively track the plant. Based on compensation control thought,through using neural network PID controller as compensation tache of traditional PID controller,a kind of hybrid PID controller is designed.Combined with neural network,self-tuning control can realized better control over non-linear system.Utilizing the advantages of fuzzy control and neural network control,a kind of neural network adaptive fuzzy controller based on fast BP algorithm is presented and it may control the plant on line.At last,the hybrid PID controller is applied in tension system of H-section continuous rolling mills and tension-free control is realized.The simulation results show that it has better performance than traditional PID controller.At the same time,it provides some reference for the control of other similar systems.