Learning-enabled Acceleration of Scenario-based Model Predictive Control

This paper presents a learning-accelerated ADMM algorithm for scenario-based model predictive control, combining parallel computing and Moreau envelope learning to reduce computation time while maintaining solution accuracy. The framework is evaluated on microgrid energy management under load and renewable generation uncertainties, with comparisons against IPOPT and MadNLP.