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2D multi-model general predictive iterative learning control for semi-batch reactor with multiple reactions
  • 时间:0
  • 分类:TD[矿业工程]
  • 作者机构:[1]College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 210009, China, [2]Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology,Kowloon, Hong Kong, China
  • 相关基金:Projects(61673205, 21727818, 61503180) supported by the National Natural Science Foundation of China; Project(2017YFB0307304)supported by National Key R&D Program of China; Project(BK20141461) supported by the Natural Science Foundation of JiangsuProvince, China
中文摘要:

Batch to batch temperature control of a semi-batch chemical reactor with heating/cooling system was discussed in this study. Without extensive modeling investigations, a two-dimensional (2D) general predictive iterative learning control (2D-MGPILC) strategy based on the multi-model with time-varying weights was introduced for optimizing the tracking performance of desired temperature profile. This strategy was modeled based on an iterative learning control (ILC) algorithm for a 2D system and designed in the generalized predictive control (GPC) framework. Firstly, a multi-model structure with time-varying weights was developed to describe the complex operation of a general semi-batch reactor. Secondly, the 2D-MGPILC algorithm was proposed to optimize simultaneously the dynamic performance along the time and batch axes. Finally, simulation for the controller design of a semi-batch reactor with multiple reactions was involved to demonstrate that the satisfactory performance could be achieved despite of the repetitive or non-repetitive disturbances.

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