| 引用本文: | 乔景慧,柴天佑.改进ELMAN网络的Q学习温度切换控制[J].控制理论与应用,2015,32(7):955~962.[点击复制] |
| QIAO Jing-hui,CHAI Tian-you.Temperature switching control integrated with improved ELMAN network and Q learning[J].Control Theory and Technology,2015,32(7):955~962.[点击复制] |
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| 改进ELMAN网络的Q学习温度切换控制 |
| Temperature switching control integrated with improved ELMAN network and Q learning |
| 摘要点击 3116 全文点击 1320 投稿时间：2014-07-10 修订日期：2015-01-27 |
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| DOI编号 10.7641/CTA.2015.40642 |
| 2015,32(7):955-962 |
| 中文关键词 料分解过程 分解炉温度 改进ELMAN网络 Q学习控制 切换控制 |
| 英文关键词 raw meal calcination process calciner temperature improved ELMAN network Q learning control switching control |
| 基金项目 国博士后科学基金项目(2014M561249, 2015T80268), 国家自然科学基金面上项目(61273177)资助. |
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| 中文摘要 |
| 在水泥生料分解过程中, 由于生料边界条件频繁变化且生料流量波动大, 使过程处于正常工况或异常工况, 致使分解炉温度和预热器C5出口温度很 难控制在工艺要求的范围内. 传统的控制方法经常导致预热器C5下料管堵塞. 为了解决上述问题, 本文提出了一个带有前馈补偿的温度智能切换控制策略, 由基于T--S的模糊控制器、基于改进的ELMAN网络的Q学习异常工况控制器和切换机制组成. 实际应用结果表明, 所提出的控制策略能够根据当前工况的变化选择正确的控制器, 并且使生产远离故障工况. |
| 英文摘要 |
| Because of the frequent change of the boundary conditions as well as the large variation in the law meal flow in calcination process, the process is often switching between the normal and abnormal working conditions. This causes difficulties to maintain the calciner temperature and the outlet temperature of preheater C5 (i.e., the No. 5 preheater) within their desired range of operations. Conventional control may even lead to operational failure, such as the clogging of feeding tube C5. To deal with this problem, we develop an intelligent temperature control scheme with feedforward compensation, which consists of a T–S-based fuzzy controller, an abnormal condition controller, an improved ELMAN network with Q learning, and a switching mechanism. The application results show that the proposed approach can select a right controller based on the current conditions to maintain the operation far from the faulty operation conditions. |
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