Policy Optimization with Differentiable MPC: Convergence Analysis under Uncertainty

Riccardo Zuliani; Efe C. Balta; John Lygeros

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arXiv · 2601.01940

Policy Optimization with Differentiable MPC: Convergence Analysis under Uncertainty

Riccardo Zuliani·Efe C. Balta·John Lygeros

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Abstract

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.

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BibTeX RIS

Riccardo Zuliani, Efe C. Balta, John Lygeros. 2026-01-05. Policy Optimization with Differentiable MPC: Convergence Analysis under Uncertainty. https://arxiv.org/abs/2601.01940

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