Design And Realization Of Air-volume Controlsystem Of Coal Mine Ventilator

The stability of mine ventilation is the basis for safety production of coal mine industry. It has been pointed out in the “Eleventh Five Years Plan Outline of National Economy and Social Development” that the ventilator in coal industry must to be innovated and optimized. As a widely used mine consumption equipment, mine ventilator is low efficiency and makes serious waste. To change the status, the thesis takes the mine ventilator variable frequency speed regulation system as the study object based on derivation of basic principle of mine ventilator energy saving, summarizing of existing methods and theoretical analyzing of vary of the power when working condition changing, by means of introducing intelligent controller to realize automatic regulation of air volume, which make the ventilator steady at the working point and reducing unnecessary waste.With their powerful ability of approximating nonlinear functions and the characteristics of adaptive learning, strong robustness and fault tolerance, neural networks have been an effective approach to model and control the unknown and uncertain nonlinear systems. Based on existing research of neural network, this thesis has taken RBF neural network which has best approximation property as the intelligent controller. However, as a highly nonlinear, strong coupling, and multi variable system, the mine ventilation is hard to establish the precise mathematical model. To solve the problem, this thesis uses the method of RBF neural network ?-th order inverse system. The inverse model of mine ventilator variable frequency speed regulation system has been set up by offline training of the RBF neural network, then the inverse system theory is used to compensated the original system to realize its linearization and decoupling. By this method, the complicated nonlinear system can be considered as a linear system.However, it is difficult to obtain a large number of training samples. The thesis takes ventilator as a 3-phase asynchronous motor and simulates its speed regulation model on Simulink with coordinate transformation and SVPWM technology. Finally, the data is acquired. With the trained offline RBF neural network and couples of integral element, it is realized the linearization and decoupling of the original system. Then using PI controller to control the pseudo linear system. The simulation results show that: compared with PID, the inverse system can track the given signal better, and show the excellent dynamic and static characteristics.At the last of this thesis, an air-volume control system has been designed for the real work field. Firstly, the ventilator monitoring and controlling system is realized by combining the programmable control technology, configuration technology. The air volume data which is collected by the system is uploaded to MATLAB with OPC communication. Processed the data by the control model which has been designed before. The processed data is transmitted to inverter to implement by PLC. At last, the automatic regulation of air-volume is realized. The experiment results showed that: the system has a short response time, small overshoot, high steady-state accuracy, good dynamic performance and a good antijamming capability through which the system can adapt to the controlled object, and meets the requirements of actual control.