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Data-driven switching modeling for MPC using Regression Trees and Random Forests
Nonlinear Analysis: Hybrid Systems ( IF 3.7 ) Pub Date : 2020-05-01 , DOI: 10.1016/j.nahs.2020.100882
Francesco Smarra , Giovanni Domenico Di Girolamo , Vittorio De Iuliis , Achin Jain , Rahul Mangharam , Alessandro D’Innocenzo

Abstract Model Predictive Control is a well consolidated technique to design optimal control strategies, leveraging the capability of a mathematical model to predict a system’s behavior over a time horizon. However, building physics-based models for complex large-scale systems can be cost and time prohibitive. To overcome this problem we propose a methodology to exploit machine learning techniques (i.e. Regression Trees and Random Forests) in order to build a Switching Affine dynamical model (deterministic and Markovian) of a large-scale system using historical data, and apply Model Predictive Control. A comparison with an optimal benchmark and related techniques is provided on an energy management system to validate the performance of the proposed methodology.

中文翻译:

使用回归树和随机森林的 MPC 数据驱动切换建模

摘要 模型预测控制是一种设计最优控制策略的综合技术,利用数学模型的能力来预测系统在时间范围内的行为。然而,为复杂的大型系统构建基于物理的模型可能成本和时间都令人望而却步。为了克服这个问题,我们提出了一种利用机器学习技术(即回归树和随机森林)的方法,以便使用历史数据构建大规模系统的切换仿射动力学模型(确定性和马尔可夫),并应用模型预测控制. 在能源管理系统上提供了与最佳基准和相关技术的比较,以验证所提出方法的性能。
更新日期:2020-05-01
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