Dual Control of Linear Systems from Bilinear Observations with Belief Space Model Predictive 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 su...
| Authors | Daniel Cao, Beixi Du, Andrew Lowitt et al. |
|---|---|
| Primary category | math.OC |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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