基于学习的场景模型预测控制加速

中文摘要 AI 辅助

基于场景的模型预测控制（SBMPC）通过在多个预测场景上优化控制动作来明确考虑不确定性，但计算复杂度随场景数量和预测时域迅速增加，限制其在实时规划和控制中的应用。本文提出一种学习加速的交替方向乘子法（ADMM）算法，通过利用并行计算和莫罗包络学习有效解决SBMPC问题，同时保持高求解精度。将SBMPC问题重新表述为可通过ADMM分解的共识形式，分离与场景相关的动态和非预期性约束，实现跨场景和时间步的并行更新，并利用现有学习优化方案加速ADMM中的原始更新以减少计算时间。在微电网能量管理问题上评估该框架，与IPOPT和MadNLP等求解器比较，显示出显著的计算加速，同时保持可靠的闭环控制性能。

英文摘要

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.

发表机构

• Department of Electrical and Computer Engineering, University of Central Florida（电气与计算机工程系，中央佛罗里达大学）

机构由 AI 辅助整理，请以论文原文为准。