# Learning-enabled Acceleration of Scenario-based Model Predictive Control

arXiv: https://arxiv.org/abs/2607.12775 · HTML: https://mlsanity.com/paper/2607.12775/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 14 Jul 2026.

## Builds on (most cited first, 13 of 13)

- [Input Convex Neural Networks](https://mlsanity.com/paper/1609.07152/) (2016)
- [Learning to Optimize: A Primer and A Benchmark](https://mlsanity.com/paper/2103.12828/) (2021)
- [Learning for Constrained Optimization: Identifying Optimal Active Constraint Sets](https://mlsanity.com/paper/1802.09639/) (2018)
- [Learning Warm-Start Points for AC Optimal Power Flow](https://mlsanity.com/paper/1905.08860/) (2019)
- [The Scenario Approach for Stochastic Model Predictive Control with Bounds on Closed-Loop Constraint Violations](https://mlsanity.com/paper/1307.5640/) (2013)
- [Linear Model Predictive Control under Continuous Path Constraints via Parallelized Primal-Dual Hybrid Gradient Algorithm](https://mlsanity.com/paper/2303.17889/) (2023)
- [Graph-Based Modeling and Decomposition of Energy Infrastructures](https://mlsanity.com/paper/2010.02404/) (2020)
- [Scenario-based Stochastic MPC for systems with uncertain dynamics](https://mlsanity.com/paper/2207.12517/) (2022)
- [Economic MPC with an Online Reference Trajectory for Battery Scheduling Considering Demand Charge Management](https://mlsanity.com/paper/2412.10851/) (2024)
- [An Improved Primal-Dual Interior Point Solver for Model Predictive Control](https://mlsanity.com/paper/1709.06362/) (2017)
- [A Distributed Active Set Method for Model Predictive Control](https://mlsanity.com/paper/2103.12681/) (2021)
- [LEAF: A Learning-Enabled ADMM Framework for Accelerated Convex Optimization](https://mlsanity.com/paper/2606.08993/) (2026)
- [AD-MPCC: Adaptive Differentiable Model Predictive Contouring Control for Autonomous Racing](https://mlsanity.com/paper/2607.00141/) (2026)

## Abstract (arXiv)

Scenario-based model predictive control (SBMPC) is a variant of model predictive control (MPC) that explicitly accounts for uncertainty by optimizing control actions over multiple predicted scenarios. However, its computational complexity increases rapidly with the number of scenarios and prediction horizon, limiting its applicability to real-time planning and control. This paper presents a learning-accelerated Alternating Direction Method of Multipliers (ADMM) algorithm for efficiently solving SBMPC problems by leveraging parallel computing and Moreau envelope learning, while maintaining high solution accuracy. We reformulate the SBMPC problems into consensus forms that can be decomposed via ADMM, separating the scenario-dependent dynamics from non-anticipativity constraints and enabling parallel updates across scenarios and time steps. Building on this decomposition, we utilize a learning-to-optimize scheme that leverages Moreau envelope learning of the cost function to accelerate the primal update in ADMM, thereby reducing computation time. The proposed framework is evaluated on a microgrid energy management problem subject to load and renewable generation uncertainties. Comparisons with IPOPT and MadNLP, two popular and modern nonlinear programming solvers, demonstrate substantial computational speedups while maintaining reliable closed-loop control performance.