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Data-driven switching modeling for MPC using Regression Trees and Random Forests
مدل سازی سوئیچینگ داده محور برای MPC با استفاده از درختان رگرسیون و جنگل های تصادفی-2020 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. Keywords: Regression Trees | Random Forests | Model predictive control | Switching systems | Markov Jump Systems |
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