摘要
针对现有中低压调压站调压精度差、可靠性差的不足,提出一种面向燃气调压器应用的RBF神经网络控制策略。其智能燃气调压器利用高阶系统的降阶近似处理方法,得到简化的电动燃气调压系统数学模型;然后,针对调压系统的非线性、不确定性特征,充分利用RBF神经网络对非线性函数良好的逼近效果,实现PID参数自整定。通过基于MSP430单片机开发板对调压器的算法性能及功能进行测试,测试结果表明,相比于传统PID控制算法,改进的算法的调节时间缩短约10%,超调量减少约6%,且抗干扰性能优越,调压器能实现数据采集、调压、串口通信、安全报警功能。
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.
作者
何进
仲元昌
孙利利
张晓帆
HE Jin;ZHONG Yuan-chang;SUN Li-li;ZHANG Xiao-fan(Chongqing Vocational Institute of Engineering,Chongqing 402260,China;School of Microelectronics and Communication Engineering,Chongqing University,Chongqing 400044,China)
出处
《计算机科学》
CSCD
北大核心
2019年第B06期138-141,共4页
Computer Science
基金
国家“973”项目(2012CB215202)
中央高校基本科研业务费专项项目(106112018CDPTCG000/41,106112017CDJZRPY0101)
重庆市科技创新专项(cstc2017shmsA40003)
重庆市教委科研项目(KJ1603206)资助
