Application Of Fuzzy CMAC Algorithm In Chain Grate Machine Pelletizing Temperature System

In the methods of pellet production,grate-rotary kiln,which have a better adaptability and the raw material cost is lower,is widely used in our country.Chain grate is an important equipment of grate-rotary kiln,and the temperature of it is the major factor that affects the quality of pellet.Now,the research of the temperature of chain grate includes the modeling of it,the controller of it and design of process parameters.And most research of the modeling of it is completed under condition that parameters are changeless.Then,the really situation of the temperature of chain grate cannot be reflected.On the other hand,the temperature of chain grate is a large dead-time controller device,and it is influenced by many parameters,so a conventional controller may not have a good control effect.In order to solve those problems,establish a model of chain grate's temperature with the changed parameters,and design a better controller.The main contents are as follows:(1)Based on the first law of thermodynamics,mechanism of heat exchanged in pellet processing of chain grate is analyzed,and equilibrium equation is established.(2)By analyzing the fuzzy control and CMAC neural network algorithm,find the following two characteristics:(1)Fuzzy in the actual production process control although the control accuracy is not high,but its reasonable control trend;(2)The drawback of CMAC neural network is trained off-line training process larger sample constraints,self-learning process in the right value for the activation of non-discriminatory amendment and output step-change.But compared with other intelligent algorithms,its unique advantage of having a fast convergence and generalization ability,similar to input similar output.Therefore,this paper combines fuzzy control and CMAC neural network?(3)Write CMAC and fuzzy CMAC algorithms using MATLAB software program,and its used to approximate the complex nonlinear function.By analyzing the simulation curve approximation,the results show that the fuzzy CMAC neural networks than the CMAC neural network has higher control precision and stronger generalization ability.The actual operation data is for the virtual system simulation.The virtual system design consists of two parts: designs the CMAC controller and fuzzy CMAC neural network controller and build LabVIEW virtual lab environment.Virtual control results show that the fuzzy CMAC neural network algorithm with high feasibility.