| Delay systems is a common industrial process, many industrial processes contain pure lag time, such as fan air volume adjustment process, smelting, chemical industry, heating furnace, is recognized as a problem in the industrial control. Because of delay phenomenon existing widely, the charged amount can’t reflect the disturbance of the system to withstand in a timely manner, leading to deterioration of the control system stability, the adjust time extended, and control adds great difficulties.General Smith prediction control is sensitive to parameters. The model match completely, system has a good amplitude margin and phase margin; otherwise, the system may not be stable. Single neuron PID forecast control algorithm is the combination of single neuron PID and Smith compensation control. Single neuron PID controller can adjust the controller parameters in real time, has good adaptive ability.Simulation results show that when the controlled object changes, the system based on single neuron PID predictive control can be reduced the overshoot, shorten the stable time.With the model error increasing, the single neuron PID forecast control system still has a larger overshoot. The single neuron PID forecast controller based on particle swarm optimization identification is designed. System parameters are corrected through the online identification. The influence for the control system caused by the controlled object changes is reduced. Simulation results show that when the model Changes, the single neuron PID forecast control system based on particle swarm identification is better able to follow a given.On the analysis of the mine main ventilator airflow closed-loop control principle, the approximate mathematical model used as control object, the effect of control method is analyzed. The simulation results show that when the controlled object changes, the single neuron PID forecast control system based on particle swarm identification has a good control effect. |
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