| 基于IMC结构的PID-GPC的鲁棒性分析 |
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| 引用本文： | 陈增强,毛宗星,杜升之,孙青林,袁著祉.基于IMC结构的PID-GPC的鲁棒性分析[J].中国化学工程学报,2003,11(1):55-61. |
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| 作者姓名： | 陈增强 毛宗星 杜升之 孙青林 袁著祉 |
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| 作者单位： | DepartmentofAutomation,Nankaiuniversity,Tianjin300071,China |
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| 基金项目： | Supported by the National Science & Technology Key Project(No. 2001BA204B01-02), the National Natural Science Foundation of China (No. 60174021) and the Key Project of Tianjin Natural Science Foundation(No. 013800711). |
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| 摘 要： | Proportion integral differential generalized predictive control(PID-GPC),a new type of generalized predictive control(GPC) is introduced,and its quality is analyzed with internal model contron(IMC).A very important characteristic,which distinguishes GPC from ordinary IMC,and the robust effect are found.At the same time,a robust region is obtained according to the control laws,so that the defect that the robust analysis could be carried out only with stable models is vercome.It is verified that the robustness of PID-GPC is stronger than general GPC.
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| 关 键 词： | IMC结构 PID-GPC 鲁棒性 化工过程控制 |
| 修稿时间： | |
Analysis of Robustness of PID-GPC Based on IMC Structure |
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| CHEN Zengqiang,MAO Zongxing,DU Shengzhi,SUN Qinglin,YUAN Zhuzhi.Analysis of Robustness of PID-GPC Based on IMC Structure[J].Chinese Journal of Chemical Engineering,2003,11(1):55-61. |
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| Authors: | CHEN Zengqiang MAO Zongxing DU Shengzhi SUN Qinglin YUAN Zhuzhi |
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| Affiliation: | Department of Automation, Nankai University, Tianjin 300071, China |
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| Abstract: | Proportion integral differential generalized predictive control(PID-GPC), a new type of generalized predictive control(GPC) is introduced, and its quality is analyzed with internal model control (IMC). A very important characteristic, which distinguishes GPC from ordinary IMC, and the robust effect are found. At the same time, a robust region is obtained according to the control laws, so that the defect that the robust analysis could be carried out only with stable models is overcome. It is verified that the robustness of PID-GPC is stronger than general GPC. |
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| Keywords: | process control internal model control predictive control |
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