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arXiv · 2603.24503

Towards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Network Modeling

Mihaela-Larisa Clement·Mónika Farsang·Agnes Poks·Johannes Edelmann·Manfred Plöchl·Radu Grosu·Ezio Bartocci
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Abstract

The practical deployment of nonlinear model predictive control (NMPC) is often limited by online computation: solving a nonlinear program at high control rates can be expensive on embedded hardware, especially when models are complex or horizons are long. Learning-based NMPC approximations shift this computation offline but typically demand large expert datasets and costly training. We propose Sequential-AMPC, a sequential neural policy that generates MPC candidate control sequences by sharing parameters across the prediction horizon. For deployment, we wrap the policy in a safety-augmented online evaluation and fallback mechanism, yielding Safe Sequential-AMPC. Compared to a naive feedforward policy baseline across several benchmarks, Sequential-AMPC requires substantially fewer expert MPC rollouts and yields candidate sequences with higher feasibility rates and improved closed-loop safety. On high-dimensional systems, it also exhibits better learning dynamics and performance in fewer epochs while maintaining stable validation improvement where the feedforward baseline can stagnate.

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Mihaela-Larisa Clement, Mónika Farsang, Agnes Poks, Johannes Edelmann, Manfred Plöchl, Radu Grosu, Ezio Bartocci. 2026-03-25. Towards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Network Modeling. https://arxiv.org/abs/2603.24503

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