Dual Control of Linear Systems from Bilinear Observations with Belief Space Model Predictive Control
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917440417431552 |
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| author | Cao, Daniel Du, Beixi Lowitt, Andrew Choi, Sunmook Dean, Sarah Sattar, Yahya |
| author_facet | Cao, Daniel Du, Beixi Lowitt, Andrew Choi, Sunmook Dean, Sarah Sattar, Yahya |
| contents | We study finite-horizon quadratic control of linear systems with bilinear observations, in which the control input affects not only the state dynamics but also the partial observations of the state. In this setting, the separation principle can fail because control inputs influence the future quality of state estimates. State estimation requires an input-dependent Kalman filter whose gain and error covariance evolve as functions of the control inputs. To address this challenge, we propose a belief-space model predictive control ($\texttt{B-MPC}$) method that plans directly over both the estimated state and its error covariance. In particular, $\texttt{B-MPC}$ plans with a deterministic surrogate of the belief evolution defined by the input-dependent Kalman filter. Through numerical experiments in two synthetic settings, we show that $\texttt{B-MPC}$ can outperform both the separation-principle controller and its MPC variant in favorable regimes, and that these gains are accompanied by lower estimation covariance and more uncertainty-aware action choices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24663 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Dual Control of Linear Systems from Bilinear Observations with Belief Space Model Predictive Control Cao, Daniel Du, Beixi Lowitt, Andrew Choi, Sunmook Dean, Sarah Sattar, Yahya Optimization and Control Machine Learning Systems and Control We study finite-horizon quadratic control of linear systems with bilinear observations, in which the control input affects not only the state dynamics but also the partial observations of the state. In this setting, the separation principle can fail because control inputs influence the future quality of state estimates. State estimation requires an input-dependent Kalman filter whose gain and error covariance evolve as functions of the control inputs. To address this challenge, we propose a belief-space model predictive control ($\texttt{B-MPC}$) method that plans directly over both the estimated state and its error covariance. In particular, $\texttt{B-MPC}$ plans with a deterministic surrogate of the belief evolution defined by the input-dependent Kalman filter. Through numerical experiments in two synthetic settings, we show that $\texttt{B-MPC}$ can outperform both the separation-principle controller and its MPC variant in favorable regimes, and that these gains are accompanied by lower estimation covariance and more uncertainty-aware action choices. |
| title | Dual Control of Linear Systems from Bilinear Observations with Belief Space Model Predictive Control |
| topic | Optimization and Control Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2604.24663 |