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作 者:RUAN Xiao-E CHEN Feng-Min WAN Bai-Wu
机构地区:[1]Department of Mathematics, Faculty of Science, Xi'an Jiaotong University, Xi'an 710049, P. R. China [2]Institute of Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, P. R. China
出 处:《自动化学报》2008年第4期426-432,共7页Acta Automatica Sinica
基 金:Supported by National Natural Science Foundation of China (F030101-60574021) and National "985" Project of China Executed in Xi'an Jiaotong University
摘 要:在为大规模工业进程的层次不变的优化编程,一种可行技术是使用真实系统的信息以便修改基于模型的最佳。在这种情形,有不同大小的步功能类型控制决定的一个序列被计算,由哪个真实系统连续地被刺激。在这份报纸,一套反复的学习控制器为大规模非线性的工业过程的一个班在分散的模式被嵌进层次不变的优化的过程。为每个分系统的控制器被用来产生升级的控制信号的一个序列以便与不同规模承担顺序的步骤控制决定的责任。学习控制设计的目的是连续地精制系统的短暂性能。借助于卷绕旋转积分的 Hausdorff 年轻的不平等,更新的规则的集中在 Lebesgue-p 标准的意义被分析。集中上的非线性和相互作用的发明被讨论。建议控制计划的有效性和有效性被一些模拟表明。In hierarchical steady-state optimization programming for large-scale industrial processes, a feasible technique is to use information of the real system so as to modify the model-based optimum. In this circumstance, a sequence of step function-type control decisions with distinct magnitudes is computed, by which the real system is stimulated consecutively. In this paper, a set of iterative learning controllers is embedded into the procedure of hierarchical steady-state optimization in decentralized mode for a class of large-scale nonlinear industrial processes. The controller for each subsystem is used to generate a sequence of upgraded control signals so as to take responsibilities of the sequential step control decisions with distinct scales. The aim of the learning control design is to consecutively refine the transient performance of the system. By means of the Hausdorff-Young inequality of convolution integral, the convergence of the updating rule is analyzed in the sense of Lebesgue-p norm. Invention of the nonlinearity and the interaction on convergence are discussed. Validity and effectiveness of the proposed control scheme are manifested by some simulations.
分 类 号:P152[天文地球—天文学] O231.2[理学—运筹学与控制论]
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