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Belief Space MPC offers improved control for linear systems with bilinear observations

Researchers have developed a belief-space model predictive control (B-MPC) method to address challenges in controlling linear systems with bilinear observations. This approach plans control inputs by considering both the estimated state and its error covariance, overcoming the failure of the separation principle where control affects observation quality. Numerical experiments demonstrate that B-MPC can outperform traditional methods by achieving lower estimation covariance and making more uncertainty-aware decisions. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Introduces a new control strategy for systems where control actions influence state estimation quality.

RANK_REASON Academic paper on a novel control method.

Read on arXiv cs.LG →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 · Daniel Cao, Beixi Du, Andrew Lowitt, Sunmook Choi, Sarah Dean, Yahya Sattar ·

    Dual Control of Linear Systems from Bilinear Observations with Belief Space Model Predictive Control

    arXiv:2604.24663v1 Announce Type: cross Abstract: 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…

  2. arXiv cs.LG TIER_1 · Yahya Sattar ·

    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 influen…