| 引用本文: | 唐斌,章云,刘国平,桂卫华.面向网络诱导时延和数据包丢失补偿的网络化广义预测控制[J].控制理论与应用,2010,27(7):880~890.[点击复制] |
| TANG Bin,ZHANG Yun,LIU Guo-ping,GUI Wei-hua.Networked generalized predictive control for compensation of network-induced time delay and packet loss[J].Control Theory and Technology,2010,27(7):880~890.[点击复制] |
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| 面向网络诱导时延和数据包丢失补偿的网络化广义预测控制 |
| Networked generalized predictive control for compensation of network-induced time delay and packet loss |
| 摘要点击 1924 全文点击 1026 投稿时间：2009-04-13 修订日期：2010-02-25 |
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| DOI编号 10.7641/j.issn.1000-8152.2010.7.CCTA090414 |
| 2010,27(7):880-890 |
| 中文关键词 网络化广义预测控制 网络诱导时延 数据包丢失 最小预测步长 预测控制增量向量 |
| 英文关键词 networked generalized predictive control network-induced time delay packet loss minimal predictive horizon vector of predictive control increment |
| 基金项目 国家自然科学基金重点资助项目(60634020); 国家自然科学基金面上资助项目(60874008); 广东省自然科学基金研究团队项目(8351009001000002); 广东省自然科学基金博士启动项目(9451009001002702). |
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| 中文摘要 |
| 针对网络化控制系统中存在的网络诱导时延和数据包丢失, 考虑了网络化广义预测控制问题. 基于多个数据打包传送的通讯方式以及网络诱导时延和数据包丢失预先可知的假设, 提出了一种采用最小预测步长和预测控制增量向量分别补偿网络诱导时延和数据包丢失对系统性能影响的新方法, 给出了相应的网络化模型预测算法和网络化滚动优化算法, 对于被控对象参数未知或缓慢变化的情况, 给出了基于递推最小二乘辨识改进算法的网络化反馈修正算法, 通过仿真验证了所提出网络化算法的有效性. |
| 英文摘要 |
| A generalized predictive control is considered for networked control systems with time-varying networkinduced time delay and packet loss. Because multiple data are sent in one packet from the source node to the target node, and the network-induced time delay and the packet loss are known a priori, a new method is proposed to compensate the influences of network-induced time delay and packet loss on control performance using minimal prediction horizon and predictive control increment vector, respectively. Thus, we develop the corresponding networked model prediction algorithm and the algorithm of networked receding-horizon optimization. For the controlled system with unknown or slowly varying parameters, the networked feedback correction algorithm is discussed based on a modified recursive leastsquares identification algorithm. Simulation results are given to show the effectiveness of our proposed algorithms. |