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CPS Theory

Artificial Intelligence and Robotics

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Full-Text Articles in Engineering

Reconfiguring Non-Convex Holes In Pivoting Modular Cube Robots, Daniel Adam Feshbach, Cynthia Sung Jul 2021

Reconfiguring Non-Convex Holes In Pivoting Modular Cube Robots, Daniel Adam Feshbach, Cynthia Sung

Lab Papers (GRASP)

We present an algorithm for self-reconfiguration of admissible 3D configurations of pivoting modular cube robots with holes of arbitrary shape and number. Cube modules move across the surface of configurations by pivoting about shared edges, enabling configurations to reshape themselves. Previous work provides a reconfiguration algorithm for admissible 3D configurations containing no non-convex holes; we improve upon this by handling arbitrary admissible 3D configurations. The key insight specifies a point in the deconstruction of layers enclosing non-convex holes at which we can pause and move inner modules out of the hole. We prove this happens early enough to maintain connectivity ...


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 ...