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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 article discusses an architecture of a digital twin for nuclear systems, integrating multiple models (physics-based and data-driven) to support decision making, state estimation, predictive control, and real-time data processing. The twin must synchronize with the physical facility faster than its operational cycle.
A new stochastic nonlinear model predictive control method for systems with additive noise is proposed. State distribution is approximated by Gaussian mixture with error bounds in Wasserstein distance. This yields closed-form expressions for expected costs and chance constraints, and the problem is solvable via nonlinear programming with correctness guarantees.
A new interdisciplinary paradigm, Dynamic Consent Engineering (DCE), is introduced, synthesizing Edward Bernays' principles of public relations with the S-E-E-D (Snowball, Equilibrium, Elasticity, Dominance) framework of system dynamics. The authors expand Bernaysian operational constraints into a four-dimensional resource matrix incorporating algorithmic media infrastructure and mathematically formalize how democratic institutions construct, optimize, and sustain political dominance.
