Safety-Constrained Optimal Control and Hamiltonian-Gradient Learning for Systems with Unknown Dynamics
eess.SY, cs.SY
Submitted: 2026-03-29
Updated: 2026-09-25
Comments: Revised and extended version with additional theoretical and experimental results submitted to ACC 2027
Code: https://github.com/Panos20102k/MultiLimo-Control
License: http://creativecommons.org/licenses/by/4.0/
The gist: We present a framework for safety-constrained optimal control when the plant dynamics are unknown and only an approximate model is available.
Terminology
Abstract
We present a framework for safety-constrained optimal control when the plant dynamics are unknown and only an approximate model is available. We apply Pontryagin's Minimum Principle to a model-based problem whose cost penalizes model-plant state mismatch and establish conditions under which the constrained model and plant Hamiltonians have the same minimizer. These conditions show that optimality can be recovered by preserving control-relevant Hamiltonian structure rather than identifying the complete plant dynamics. Motivated by this observation, we develop a fully online method that uses integral trajectory data and policy iteration to learn the dynamics-dependent factors of the plant Hamiltonian gradient directly. Experiments on robotic cruise control demonstrate constrained model-plant control equivalence and online Hamiltonian-gradient reconstruction.
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