Open Access. Powered by Scholars. Published by Universities.®

Engineering Commons

Open Access. Powered by Scholars. Published by Universities.®

Series

PDF

CPS Theory

Controls and Control Theory

Articles 1 - 3 of 3

Full-Text Articles in Engineering

Data-Driven Model Predictive Control Using Random Forests For Building Energy Optimization And Climate Control, Francesco Smarra, Achin Jain, Tullio De Rubeis, Dario Ambrosini, Alessandro D'Innocenzo, Rahul Mangharam Apr 2018

Data-Driven Model Predictive Control Using Random Forests For Building Energy Optimization And Climate Control, Francesco Smarra, Achin Jain, Tullio De Rubeis, Dario Ambrosini, Alessandro D'Innocenzo, Rahul Mangharam

Real-Time and Embedded Systems Lab (mLAB)

Model Predictive Control (MPC) is a model-based technique widely and successfully used over the past years to improve control systems performance. A key factor prohibiting the widespread adoption of MPC for complex systems such as buildings is related to the difficulties (cost, time and effort) associated with the identification of a predictive model of a building. To overcome this problem, we introduce a novel idea for predictive control based on historical building data leveraging machine learning algorithms like regression trees and random forests. We call this approach Data-driven model Predictive Control (DPC), and we apply it to three different case ...


Data-Driven Switched Affine Modeling For Model Predictive Control, Francesco Smarra, Achin Jain, Rahul Mangharam, Alessandro D'Innocenzo Apr 2018

Data-Driven Switched Affine Modeling For Model Predictive Control, Francesco Smarra, Achin Jain, Rahul Mangharam, Alessandro D'Innocenzo

Real-Time and Embedded Systems Lab (mLAB)

Model Predictive Control (MPC) is a well-consolidated technique to design optimal control strategies, leveraging the capability of a mathematical model to predict the system’s behavior over a predictive horizon. However, building physics-based models for large-scale systems, such as buildings and process control, can be cost and time prohibitive. To overcome this problem we propose in this paper a methodology to exploit machine learning techniques (i.e. regression trees and random forests) in order to build a state-space switched affine dynamical model of a large scale system only using historical data. Finite Receding Horizon Control (RHC) setup using control-oriented data-driven ...


Learning And Control Using Gaussian Processes, Achin Jain, Truong X Nghiem, Manfred Morari, Rahul Mangharam Feb 2018

Learning And Control Using Gaussian Processes, Achin Jain, Truong X Nghiem, Manfred Morari, Rahul Mangharam

Real-Time and Embedded Systems Lab (mLAB)

Building physics-based models of complex physical systems like buildings and chemical plants is extremely cost and time prohibitive for applications such as real-time optimal control, production planning and supply chain logistics. Machine learning algorithms can reduce this cost and time complexity, and are, consequently, more scalable for large-scale physical systems. However, there are many practical challenges that must be addressed before employing machine learning for closed-loop control. This paper proposes the use of Gaussian Processes (GP) for learning control-oriented models: (1) We develop methods for the optimal experiment design (OED) of functional tests to learn models of a physical system ...