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 is 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
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 47%AgileRL/AgileRL →
- PossiblePossibly related (embedding) · 46%AgentCore-8B →
- LinkedLinked via arxiv author · 85%Trinh Tran →
“Learning-enabled Acceleration of Scenario-based Model Predictive Control”
- LinkedLinked via arxiv author · 85%Binh Nguyen →
“Learning-enabled Acceleration of Scenario-based Model Predictive Control”
- LinkedLinked via arxiv author · 85%Truong X. Nghiem →
“Learning-enabled Acceleration of Scenario-based Model Predictive Control”