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This paper proposes a meta-control architecture using temporal self-attention for adaptive control of Euler-Lagrange systems with unobservable memory states. It demonstrates improved tracking performance over baseline methods on a 2-DOF manipulator while identifying failure modes in long-memory regimes.
This paper introduces SHAPE, a structured adaptive port-Hamiltonian optimizer for fixed-budget nonconvex optimization that uses event-triggered mechanisms to balance descent, exploration, and budget allocation.