Decoupling Control System Of Ball Mill Based On BELBIC And FNN

At present the majority are relying on thermal power plants burning coal for power generation,in order to achieve maximum utilization of coal energy,coal power plants must be ground into a pulverized coal combustion.In the coal grinding process,ball mill in power plant plays a very important role,it is the fire power plants,smelting ores and other materials grinding process important equipment.Mill in the plant during operation a large amount of energy,while its milling control system is a nonlinear,time delay,large inertia of the system,there are many uncertainties disturbance variable exists in the system;the use of conventional control the method is difficult to mill system to achieve good quality control,so we need to explore and study a new intelligent control scheme applied to ball mill control system to fix it when milling operation control problems.Brain emotional learning intelligent controller is a new controller,the theoretical foundation established in his brain emotional learning,as researchers continue to study in depth,BELBIC controller received a lot of attention and are used in many control systems.This article will BELBIC control systems applied to the ball to go,and compared to traditional control methods;the use of teaching and learning optimization algorithm tunable BELBIC intelligent controller input signal optimization;while adding Fuzzy Neural Network(FNN),solve mill controlled coupling relationship between variables;main work is as follows:(1)Learn the main ball milling process work flow and configuration parameters themselves;highlighted several conventional control method using a ball mill now,do not make a ball mill to achieve good control effect,in order to overcome this problem,we need to work out a new type of control scheme.Detailed analysis of the interaction relationship between the variables mill system,obtained by reading literature mathematical reference model ideal ball control system.(2)Details of the physiology and mathematical description of the method of brain emotional learning models,as well as BEL thalamus and the orbitofrontal cortex weight regulating law;On this basis,we propose a new type of BELBIC controller.(3)The use of teaching and learning algorithm(TLBO)optimization BELBIC model parameters.Parameters for use by TLBO optimization algorithm BEL model parameters were optimized and the optimized control system,effectively overcome the adverse effects of BELBIC parameters set at random to bring improved performance ball mill control system.(4)For mill existence multivariable,strong coupling characteristics,based on TLBO optimization BEBIC model parameters,the presence of a ball mill variable coupling between the use of Fuzzy Neural Network(FNN)decoupling;proposed based on FNN and the ball BELBIC decoupling control programs;and simulation experiment results show that BELBIC controller has strong robustness.