Nerve Network Predictive Control Crude Oil Heater Stove Temperature

Recently for complex industrial process and higher control effect systems, traditional PID control can not meet needs in control accuracy and control quality in strong nonlinear systems. Therefore there is a strong need of advanced control strategies to displace traditional general control.Model predictive control based on neural network is a more accurate method posed for strong nonlinear systems whose parameters are time-varying or structure is unknown. Traditional general control effect is out of satisfaction in control accuracy for strong nonlinear systems, so neural network draws attention because of its particular advantages in approach of nonlinear function. Neural network that has characters of robustness and fault-tolerant can be used to describe strong nonlinear dynamic process, which makes it widely applied to control industry produce process.In the environment of MATLAB/SIMULINK, neural network is adopted in the paper to identify the plant of control of reactor concentration control which is a strong nonlinear system, on the basis of which optimized controller is designed to realize nonlinear process predictive control based on artificial neural network. In the paper by analyzing and studying the method of choosing of parameters in process identification and optimized controller, a conclusion is drawn that the control effect is of satisfactory in accuracy. By turning to Dynamic Data Exchange between Kingview and MATLAB, real-time simulation of reactor concentration predictive control is realized.