The Arrow of Time in Estimation and Control: Duality Theory Beyond the Linear Gaussian Model

被引:0
|
作者
Kim, Jin Won [1 ,2 ]
Mehta, Prashant G. [3 ,4 ]
机构
[1] Hongik Univ, Mech & Syst Design Engn, Seoul 04066, South Korea
[2] Univ Potsdam, Inst Math, Potsdam, Germany
[3] Univ Illinois, Mech Sci & Engn, Champaign, IL 61801 USA
[4] Univ Illinois, United Technol Res Ctr UTRC, East Hartford, CT USA
来源
IEEE CONTROL SYSTEMS MAGAZINE | 2025年 / 45卷 / 02期
关键词
Linear systems; Stochastic systems; Hidden Markov models; Estimation; Observability; Gaussian processes; Control theory;
D O I
10.1109/MCS.2025.3534496
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The duality between estimation and control is a foundational concept in control theory. Most students learn about the elementary duality-between observability and controllability-in their first graduate course in linear systems theory. Therefore, it comes as a surprise that for a more general class of nonlinear stochastic systems (HMMs), duality is incomplete. Our objective in writing this article is twofold: 1) to describe the difficulty in extending duality to HMMs and 2) to discuss its recent resolution by the authors. A key message is that the main difficulty in extending duality comes from time reversal when going from estimation to control. The reason for time reversal is explained with the aid of the familiar linear deterministic and linear Gaussian models. The explanation is used to motivate the difference between the linear and the nonlinear models. Once the difference is understood, duality for HMMs is described based on our recent work. The article also includes a comparison and discussion of the different types of duality considered in the literature.
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页码:70 / 90
页数:21
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