Policy Optimization with Differentiable MPC: Convergence Analysis under Uncertainty

Fuente: arXiv
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Hauptverfasser: Zuliani, Riccardo, Balta, Efe C., Lygeros, John
Format: Preprint
Veröffentlicht: 2026
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author Zuliani, Riccardo
Balta, Efe C.
Lygeros, John
author_facet Zuliani, Riccardo
Balta, Efe C.
Lygeros, John
contents Model-based policy optimization is a well-established framework for designing reliable and high-performance controllers across a wide range of control applications. Recently, this approach has been extended to model predictive control policies, where explicit dynamical models are embedded within the control law. However, the performance of the resulting controllers, and the convergence of the associated optimization algorithms, critically depends on the accuracy of the models. In this paper, we demonstrate that combining gradient-based policy optimization with recursive system identification ensures convergence to an optimal controller design and showcase our finding in several control examples.
format Preprint
id arxiv_https___arxiv_org_abs_2601_01940
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Policy Optimization with Differentiable MPC: Convergence Analysis under Uncertainty
Zuliani, Riccardo
Balta, Efe C.
Lygeros, John
Systems and Control
Optimization and Control
Model-based policy optimization is a well-established framework for designing reliable and high-performance controllers across a wide range of control applications. Recently, this approach has been extended to model predictive control policies, where explicit dynamical models are embedded within the control law. However, the performance of the resulting controllers, and the convergence of the associated optimization algorithms, critically depends on the accuracy of the models. In this paper, we demonstrate that combining gradient-based policy optimization with recursive system identification ensures convergence to an optimal controller design and showcase our finding in several control examples.
title Policy Optimization with Differentiable MPC: Convergence Analysis under Uncertainty
topic Systems and Control
Optimization and Control
url https://arxiv.org/abs/2601.01940