LQG solution for POMDP without estimating states: A minimum variance approach
LQG solution for POMDP without estimating states: A minimum variance approach
This paper presents a method to control discrete-time linear time-invariant systems with incomplete or corrupted measurements by designing a Linear Quadratic Gaussian controller that does not rely on explicit state estimation. Using minimum variance duality, the current control input is expressed as a linear function of available measurements and past inputs, reducing the problem to a tractable deterministic optimization. Theoretical justification and numerical experiments demonstrate the approach's effectiveness.
The authors propose a theoretical framework for controlling discrete-time linear time-invariant systems under incomplete measurements by formulating the Linear Quadratic Gaussian problem as a deterministic optimization task without explicit state estimation, validated through numerical experiments.
Source: arXiv Robotics — research abstracts · Read original article ↗
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arXiv:2607.12135v2 Announce Type: replace-cross Abstract: This paper investigates the control of discrete-time linear time-invariant (LTI) systems subject to incomplete and corrupted measurements. Specifically, we focus on designing a Linear Quadratic Gaussian (LQG) controller without relying on explicit state estimation. By leveraging minimum variance duality, our approach allows the current control input to be repr
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Source：arXiv Robotics — research abstracts · arxiv.org