首页    期刊浏览 2025年02月10日 星期一
登录注册

文章基本信息

  • 标题:Learning Approximate Semi-Explicit Hybrid MPC with an Application to Microgrids ⁎ ⁎⁎ ⁎⁎ D. Masti and T. Pippia have contributed equally to this work.
  • 本地全文:下载
  • 作者:Daniele Masti ; Tomas Pippia ; Alberto Bemporad
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2020
  • 卷号:53
  • 期号:2
  • 页码:5207-5212
  • DOI:10.1016/j.ifacol.2020.12.1192
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractWe present a semi-explicit formulation of model predictive controllers for hybrid systems with feasibility guarantees. The key idea is to use a machine-learning approach to learn a compact predictor of the integer/binary components of optimal solutions of the multiparametric mixed-integer linear optimization problem associated with the controller, so that, on-line, only a linear programming problem must be solved. In this scheme, feasibility is ensured by a simple rule-based engine that corrects the binary configuration only when necessary. The performance of the approach is assessed on a well known benchmark for which explicit controllers based on domain-specific knowledge are already available. Simulation results show how our proposed method considerably lowers computation time without deteriorating closed-loop performance.
  • 关键词:KeywordsModel Predictive ControlMachine LearningMixed-Integer OptimizationModelingSimulation of Power Systems
国家哲学社会科学文献中心版权所有