| 基于失配补偿Smith-RBF神经网络的 主蒸汽压力控制技术 |
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| 引用本文： | 高锦,章家岩,冯旭刚,姚凤麒.基于失配补偿Smith-RBF神经网络的 主蒸汽压力控制技术[J].重庆大学学报(自然科学版),2019,42(7):105-113. |
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| 作者姓名： | 高锦 章家岩 冯旭刚 姚凤麒 |
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| 作者单位： | 安徽工业大学 电气与信息工程学院,安徽 马鞍山,243032;安徽工业大学 电气与信息工程学院,安徽 马鞍山,243032;安徽工业大学 电气与信息工程学院,安徽 马鞍山,243032;安徽工业大学 电气与信息工程学院,安徽 马鞍山,243032 |
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| 基金项目： | 安徽省重点研究与开发计划项目（1804a09020094）；安徽省高校自然科学研究重点项目（KJ2018A0054，KJ2018A0060）。 |
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| 摘 要： | 针对燃气发电锅炉主蒸汽压力控制系统对象的大滞后、不确定性和煤气扰动大的特点,设计了一种基于失配补偿Smith预估及RBF神经网络的控制方案。利用RBF神经网络的在线学习能力整定常规PID的参数,并通过失配补偿Smith预估控制器对系统中存在的纯滞后进行补偿,有效解决了火力发电锅炉主蒸汽压力对象动态特性模型失配及纯滞后的问题。通过仿真研究及实际应用表明:该控制方法对于火力发电锅炉主蒸汽压力控制具有很好的稳定性和抗干扰能力。
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| 关 键 词： | 锅炉 主蒸汽压力 神经网络 失配补偿Smith预估 |
| 收稿时间： | 2018/7/29 0:00:00 |
Main steam pressure control technique based on mismatch compensation Smith predictor and RBF neural network |
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| GAO Jin,ZHANG Jiayan,FENG Xugang and YAO Fengqi.Main steam pressure control technique based on mismatch compensation Smith predictor and RBF neural network[J].Journal of Chongqing University(Natural Science Edition),2019,42(7):105-113. |
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| Authors: | GAO Jin ZHANG Jiayan FENG Xugang and YAO Fengqi |
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| Institution: | School of Electrical and Information Engineering, Anhui University of Technology, Maanshan 243002, Anhui, P. R. China,School of Electrical and Information Engineering, Anhui University of Technology, Maanshan 243002, Anhui, P. R. China,School of Electrical and Information Engineering, Anhui University of Technology, Maanshan 243002, Anhui, P. R. China and School of Electrical and Information Engineering, Anhui University of Technology, Maanshan 243002, Anhui, P. R. China |
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| Abstract: | To solve the problems of large lag, uncertainty and gas disturbance of main steam pressure control system object of gas-fired power boiler,a control scheme based on mismatch compensation Smith prediction and RBF neural network is designed. The RBF neural network''s online learning ability is used to adjust the parameters of the conventional PID, and the mismatch compensation Smith prediction controller to compensate the pure hysteresis in the system.The improved algorithm effectively solves the problem of mismatch of dynamic characteristic model and pure lag of main steam pressure object in thermal power boiler. The simulation research and practical application show that the control method has good stability and anti-interference ability for the main steam pressure control of thermal power boilers. |
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| Keywords: | boiler main steam pressure neural network mismatch compensation Smith prediction |
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