Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 138-141.

• Intelligent Computing • Previous Articles     Next Articles

RBF Artificial Intelligence Control Strategy for Gas Pressure Regulating Application

HE Jin1, ZHONG Yuan-chang2, SUN Li-li2, ZHANG Xiao-fan2   

  1. Chongqing Vocational Institute of Engineering,Chongqing 402260,China1;
    School of Microelectronics and Communication Engineering,Chongqing University,Chongqing 400044,China2
  • Online:2019-06-14 Published:2019-07-02

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Abstract: In order to overcome the shortcomings of poor accuracy and reliability of the existing medium and low voltage regulator stations,a RBF neural network control strategy for gas regulator application was proposed.The intelligent gas regulator uses the reduced order approximation method of high-order system to obtain a simplified mathematical model of electric gas regulator system.Then,according to the characteristics of non-linearity and uncertainty of the regulator system,it makes full use of the good approximation effect of RBF neural network for the non-linear function to realize the self-tuning of PID parameters.The performance and function of the voltage regulator are tested based on MSP430 MCU development board.The test results show that compared with the traditional PID control algorithm,the improved algorithm reduces the adjustment time by about 10% and the overshoot by about 6%,and the anti-interference performance is superior.The voltage regulator can realize data acquisition,voltage regulation,serial communication and safety alarm functions.

Key words: Intelligent gas regulator, MSP430, Neural network, PID control

CLC Number: 

  • TP181
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