Zero-Shot Function Encoder-Based Differentiable Predictive Control

Hassan Iqbal, Xingjian Li, Tyler Ingebrand, Adam Thorpe, Krishna Kumar, Ufuk Topcu, Jan Drgona
Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR 331:299-315, 2026.

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 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v331-iqbal26a, title = {Zero-Shot Function Encoder-Based Differentiable Predictive Control}, author = {Iqbal, Hassan and Li, Xingjian and Ingebrand, Tyler and Thorpe, Adam and Kumar, Krishna and Topcu, Ufuk and Drgona, Jan}, booktitle = {Proceedings of The 8th Annual Learning for Dynamics and Control Conference}, pages = {299--315}, year = {2026}, editor = {Sukhatme, Gaurav and Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}, volume = {331}, series = {Proceedings of Machine Learning Research}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v331/main/assets/iqbal26a/iqbal26a.pdf}, url = {https://proceedings.mlr.press/v331/iqbal26a.html}, 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 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.} }
Endnote
%0 Conference Paper %T Zero-Shot Function Encoder-Based Differentiable Predictive Control %A Hassan Iqbal %A Xingjian Li %A Tyler Ingebrand %A Adam Thorpe %A Krishna Kumar %A Ufuk Topcu %A Jan Drgona %B Proceedings of The 8th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2026 %E Gaurav Sukhatme %E Lars Lindemann %E Stephen Tu %E Adam Wierman %E Nikolay Atanasov %F pmlr-v331-iqbal26a %I PMLR %P 299--315 %U https://proceedings.mlr.press/v331/iqbal26a.html %V 331 %X 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 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.
APA
Iqbal, H., Li, X., Ingebrand, T., Thorpe, A., Kumar, K., Topcu, U. & Drgona, J.. (2026). Zero-Shot Function Encoder-Based Differentiable Predictive Control. Proceedings of The 8th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 331:299-315 Available from https://proceedings.mlr.press/v331/iqbal26a.html.

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