| 引用本文: | 陈彦桥,王印松,刘吉臻,曾德良.基于PID型模糊神经网络的火电站汽包压力控制[J].控制理论与应用,2003,20(4):627~629.[点击复制] |
| CHEN Yan-qiao,WANG Yin-song,LIU Ji-zhen,ZENG De-liang.Drum pressure control based on PID-type fuzzy neural network in fossil-fired electric power station[J].Control Theory and Technology,2003,20(4):627~629.[点击复制] |
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| 基于PID型模糊神经网络的火电站汽包压力控制 |
| Drum pressure control based on PID-type fuzzy neural network in fossil-fired electric power station |
| 摘要点击 1712 全文点击 1157 投稿时间：2001-06-23 修订日期：2002-06-06 |
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| DOI编号 |
| 2003,20(4):627-629 |
| 中文关键词 汽包压力 PID型模糊神经网络 自学习 时变性 |
| 英文关键词 drum pressure PID-type fuzzy neural networks self-learning time-variable |
| 基金项目 国家电力公司重大科技项目(SP11-2001-02036)资助项目. |
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| 中文摘要 |
| 为克服火电站燃料-汽包压力调节对象的非线性、时变和纯迟延特性, 采用含自回归神经元的PID型模糊神经网络作为汽包压力控制器, 进行协调控制系统的设计. 仿真研究表明, 这种初值易选、学习能力较强的模糊神经网络控制器可以克服该对象的时变性和随机性干扰, 大大改善控制品质. |
| 英文摘要 |
| In order to overcome the nonlinearization, time-variability and pure lag of fuel-drum pressure control loop in the power station, a PID-type fuzzy neural network with a self-regress nerve cell was proposed as the drum pressure controller in the coordinate control system. The simulation research shows that the time-variability and random disturbance can be overcome by the controller, which is easy to select the initial value and capable of self-learning and thus considerably improves control quality. |
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