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Information-Aware Model Predictive Control for Satellite Inspection
US & West ·
A MPC framework for autonomous satellite inspection incorporates estimation covariance into the control objective, merging control and state estimation. The covariance evolves via a linear Kalman filter, and the measurement model depends on relative geometry. This optimizes trajectories for informative data collection while satisfying safety constraints.
Why it matters
The approach demonstrates integration of estimation and control to improve data collection efficiency, relevant for asset monitoring and control tasks.
The paper establishes deterministic feasibility guarantees for differentiable predictive control (DPC), a self-supervised approach for approximating explicit MPC policies. Through topological analysis of the induced reachable safe set, it shows DPC can guarantee feasibility without online safety filters.
A data-driven reachability analysis method computes over-approximations of reachable sets directly from online state measurements, without requiring an accurate dynamic model. It estimates time-varying unknown models using an exponentially forgetting zonotopic recursive least squares method that handles bounded noise.
The company Aktiv published the final document of a foresight session on the future of cyber-physical systems security in Russia. The document provides strategic recommendations and industry forecasts up to 2040.
