Control performance assessment for ILC-controlled batch processes in two-dimensional system framework
Control performance assessment for ILC-controlled batch processes in two-dimensional system framework
复制标题
二维系统框架中 ILC 控制批处理的控制性能评估
DOI：
10.1109/tsmc.2017.2672563
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
黄彪
中科院分区：
文献类型：
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作者：
王友清;张浩;魏少龙;周东华;黄彪
本文研究了迭代学习控制（ILC）控制的批处理过程的控制性能评价（CPA）问题。提出了一个二维线性二次高斯（LQG）基准来评估二维框架下ILC的性能。基于二维理论，首先将ilc控制的批处理过程转换为二维Roesser模型。随后，为了评估转换后的二维系统的控制性能，将传统的LQG权衡曲线升级为LQG性能评估权衡曲面。然而，要获得LQG权衡面，需要完整的系统模型知识。针对缺乏精确模型知识的系统，进一步提出了一种新的数据驱动CPA方法。在这种情况下，提出了一种新的二维闭环子空间识别方法来识别转换后的二维Roesser系统。基于辨识出的模型，可以得到LQG权衡面，并利用该权衡面来评估控制性能。通过仿真算例验证了该方法的可行性和有效性。
In this paper, control performance assessment (CPA) is studied for batch processes controlled by iterative learning control (ILC). A 2-D linear quadratic Gaussian (LQG) benchmark is proposed to assess the performance of ILC in a 2-D framework. Based on the 2-D theory, an ILC-controlled batch process is first converted into a 2-D Roesser model. Subsequently, in order to assess the control performance of the converted 2-D system, the conventional LQG tradeoff curve is upgraded to the LQG performance assessment tradeoff surface. However, the complete knowledge of the system model is required to obtain the LQG tradeoff surface. For system without accurate model knowledge, a novel data-driven CPA method is further proposed. In this case, a novel 2-D closed-loop subspace identification method is proposed to identify the converted 2-D Roesser system. Based on the identified model, the LQG tradeoff surface can be obtained and utilized to assess the control performance. Overall, several simulation examples verified the feasibility and effectiveness of the proposed method.