Optimal Iterative Learning Control for Product Qualities in Batch Processes Based on Generalized Hinging Hyperplanes

摘要：

A generalized hinging hyperplanes (GHH) based iterative learning control (ILC) strategy is proposed to improve product qualities of batch processes. The optimal ILC for batch process is usually based on linear model, but GHH is introduced here to construct the nonlinear dynamic model of batch process to improve the model accuracy. Because GHH is a kind of piecewise linear model, its gradient information can be obtained explicitly. With a quadratic objective function in the optimal ILC, the input of the next batch can be calculated analytically based on the linearization of GHH model. The output tracking error can be gradually reduced under the GHH-ILC method. This proposed scheme is illustrated on a typical batch polymerization reactor, and simulation results show that the GHH-ILC method can obtain better tracking performance.

关键词： Iterative learning control generalized hinging hyperplanes batch process tracking error

作者: YU Xiaodong XIONG Zhihua HUANG Dexian JIANG Yongheng

作者单位: Department of Automation, Tsinghua University, Beijing 100084, China Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China

会议类型: 国际会议

会议名称: The 31st Chinese Control Conference(第三十一届中国控制会议)

会议地点: 合肥

会议语种:英文

页码: 3093-3098

在线出版日期: 2012-07-01（万方平台首次上网日期，不代表论文的发表时间）