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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.
Industrial environments are increasingly characterized by the tight interaction among physical processes, communication infrastructures, and intelligent applications. In this context, Digital Twins (DTs) have emerged as a key technology for system analysis and optimization.
A ruleless digital twin approach is proposed where decisions are generated automatically from purely declarative user specifications instead of hand-written rules. This reduces the complexity of building and maintaining twins under dynamically changing conditions such as weather or dynamic energy pricing.
A calibration and control method is developed for a multiscale bioprocess digital twin using sparse observations, quasi-likelihood estimation and adjoint sensitivity analysis. Parameter uncertainty then feeds into policy optimization and adaptive experiment design.
