[2603.24503] Towards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Network Modeling

Computer Science > Machine Learning arXiv:2603.24503 (cs) [Submitted on 25 Mar 2026] Title:Towards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Network Modeling Authors:Mihaela-Larisa Clement, Mónika Farsang, Agnes Poks, Johannes Edelmann, Manfred Plöchl, Radu Grosu, Ezio Bartocci View a PDF of the paper titled Towards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Network Modeling, by Mihaela-Larisa Clement and 6 other authors View PDF HTML (experimental) 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...