Year: 2025
Type: preprint
Abstract: We introduce a differentiable framework for zero-shot adaptive control over parametric families of nonlinear dynamical systems. Our approach integrates a function encoder-based neural ODE (FE-NODE) for modeling system dynamics with a differentiable predictive control (DPC) for offline self-supervised learning of explicit control policies. The FE-NODE captures nonlinear behaviors in state transitions and enables zero-shot adaptation to new systems without retraining, while the DPC efficiently learns control policies across system parameterizations, thus eliminating costly online optimization common in classical model predictive control. We demonstrate the efficiency, accuracy, and online adaptability of the proposed method across a range of nonlinear systems with varying parametric scenarios, highlighting its potential as a general-purpose tool for fast zero-shot adaptive control.
Source: arXiv (Cornell University)
Authors: Iqbal, Hassan, Li, Xingjian, Ingebrand, Tyler, Thorpe, Adam, Kumar, Krishna, Topcu, Ufuk, Drgoňa, Ján
Institution: The University of Texas at Austin
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: G2515541506, SCIPE: Chishiki.ai: A sustainable, diverse, and integrated CIP community for Artificial Intelligence in Civil and Environmental Engineering, CAREER: HayaRupu: Accelerating Natural Hazard Engineering with AI-Driven Discovery Loops, G6025125751, Physics-informed Learning for Dynamical Systems from Scarce Data, G8876996369
OpenAlex ID: W7104974814
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