AI & ML
Featurized Occupation Measures for Structured Global Search in Numerical Optimal Control
arXiv eess.SY0 views
arXiv:2603.16231v3 Announce Type: replace-cross Abstract: Numerical optimal control is split between globally structured but dimensionally intractable Hamilton--Jacobi--Bellman (HJB) methods and scalable but local trajectory optimization. We introduce Featurized Occupation Measures (FOM), a finite-dimensional primal--dual interface where HJB subsolutions guide primal search and primal occupation data can inform certificate refinement. Explicit and implicit realizations, based on finite Liouville tests and rollout-sampled residuals, are proved asymptotically consistent with the exact occupation-measure LP. FOM also identifies when HJB-type certificates avoid full state-space representation. For factor graphs induced by compatible passivity-based interconnections, blockwise HJB inequalities yield block-additive OM-dual certificates, including blockwise approximate variants. The curse of dimensionality is then shifted from state dimension to interconnection topology. Approximate certificates remain reusable under time shifts and bounded model perturbations, with explicit degradation bounds. A static obstacle-avoidance benchmark illustrates how tighter certificates improve sample-based global search.
Source:https://arxiv.org/abs/2603.16231PreviousInfinite-Dimensional Closed-Loop Inverse Kinematics for Soft Robots via Neural OperatorsNextKoopman-Based Nonlinear Identification and Model Predictive Control of a Turbofan Engine
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