Learning-enabled Acceleration of Scenario-based Model Predictive Control
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...
| Authors | Trinh Tran, Binh Nguyen, Truong X. Nghiem |
|---|---|
| Primary category | math.OC |
| Published | 14 July 2026 |
| Last updated at source | 5 September 2026 |
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