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

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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Publication Details

Journal
IEEE Control Systems
Published
2025-04-01
DOI
https://doi.org/10.1109/mcs.2025.3534496
Primary Topic
Advanced Control Systems Optimization
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
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article

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

Prashant G. Mehta, Jin Won Kim
IEEE Control Systems
Advanced Control Systems Optimization
article

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

Prashant G. Mehta, Jin Won Kim
article en

Abstract

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.

IEEE Control SystemsVol. 45(2)
University of Illinois Urbana-Champaign (US), Hongik University (KR)
National Science Foundation, California Department of Fish and Game, Air Force Office of Scientific Research
Openalex Percentile: Top 94%
Advanced Control Systems Optimization
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