随机变量
控制理论(社会学)
度量(数据仓库)
数学优化
熵(时间箭头)
数学
实现(概率)
概率分布
概率密度函数
随机过程
随机控制
计算机科学
有界函数
控制器(灌溉)
缩小
最优控制
控制(管理)
统计
人工智能
物理
生物
数据库
数学分析
量子力学
农学
作者
Jie Zhou,X. Wang,J. F. Zhang,H. Wang,Guang-Hong Yang
出处
期刊:IEEE Transactions on Automatic Control
[Institute of Electrical and Electronics Engineers]
日期:2015-09-01
卷期号:60 (9): 2524-2529
被引量:13
标识
DOI:10.1109/tac.2014.2382151
摘要
Minimization of output uncertainty is an important control target for stochastic systems subject to bounded random inputs. Firstly, causes that prevent realization of the minimum Shannon entropy (SE) control are examined based on the analysis of the SE definition in the continuous random variable (CRV) and on this basis, a new measure of uncertainty, which is called rational entropy (RE), is proposed, and the key properties of the RE are proved. Next, results are extended to the output stochastic distribution control (SDC) systems whose output probability density functions (PDFs) are approximated by a linear B-spline basis functions model. Then, two types of minimum RE controller with the mean constraint are given and several controller performance assessment (CPA) benchmarks for output SDC systems are presented. Finally, simulations are included to discuss the feasibility and effectiveness of the proposed measure of uncertainty and performance assessment methods.
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