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Data-driven switching modeling for MPC using Regression Trees and Random Forests
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DOI：10.1016/j.nahs.2020.100882.png)
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Abstract
En 中文
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. (c) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Regression Trees
Random Forests
Model predictive control
Switching systems
Markov Jump Systems
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