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 CPS Efficient Buildings (3)
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 CPS RealTime (2)

 CPS Formal Methods (2)
 CPS Theory (2)
 Switched systems (1)
 Arrhythmia monitoring (1)
 Datadriven model predictive control (1)
 Innetwork control (1)
 Formal verification (1)
 Random forests (1)
 Cooperative control (1)
 Tachycardia (1)
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 Realtime (1)
 Predictive control (1)
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 Streaming languages (1)
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 Reachability (1)
 Networked control systems (1)
 Autonomous vehicles (1)
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 Receding horizon control (1)
Articles 1  6 of 6
FullText Articles in Engineering
DataDriven 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
DataDriven 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
RealTime and Embedded Systems Lab (mLAB)
Model Predictive Control (MPC) is a modelbased 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 Datadriven model Predictive Control (DPC), and we apply it to three different case ...
DataDriven Switched Affine Modeling For Model Predictive Control, Francesco Smarra, Achin Jain, Rahul Mangharam, Alessandro D'Innocenzo
DataDriven Switched Affine Modeling For Model Predictive Control, Francesco Smarra, Achin Jain, Rahul Mangharam, Alessandro D'Innocenzo
RealTime and Embedded Systems Lab (mLAB)
Model Predictive Control (MPC) is a wellconsolidated technique to design optimal control strategies, leveraging the capability of a mathematical model to predict the systemâ€™s behavior over a predictive horizon. However, building physicsbased models for largescale 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 statespace switched affine dynamical model of a large scale system only using historical data. Finite Receding Horizon Control (RHC) setup using controloriented datadriven ...
RealTime Decision Policies With Predictable Performance, Houssam Abbas, Rajeev Alur, Konstantinos Mamouras, Rahul Mangharam, Alena Rodionova
RealTime Decision Policies With Predictable Performance, Houssam Abbas, Rajeev Alur, Konstantinos Mamouras, Rahul Mangharam, Alena Rodionova
RealTime and Embedded Systems Lab (mLAB)
As methods and tools for CyberPhysical Systems grow in capabilities and use, onesizefitsall solutions start to show their limitations. In particular, tools and languages for programming an algorithm or modeling a CPS that are specific to the application domain are typically more usable, and yield better performance, than generalpurpose languages and tools. In the domain of cardiac arrhythmia monitoring, a small, implantable medical device continuously monitors the patient's cardiac rhythm and delivers electrical therapy when needed. The algorithms executed by these devices are streaming algorithms, so they are best programmed in a streaming language that allows the programmer to ...
Data Predictive Control Using Regression Trees And Ensemble Learning, Achin Jain, Francesco Smarra, Rahul Mangharam
Data Predictive Control Using Regression Trees And Ensemble Learning, Achin Jain, Francesco Smarra, Rahul Mangharam
RealTime and Embedded Systems Lab (mLAB)
Decisions on how to best operate large complex plants such as natural gas processing, oil refineries, and energy efficient buildings are becoming ever so complex that modelbased predictive control (MPC) algorithms must play an important role. However, a key factor prohibiting the widespread adoption of MPC, is the cost, time, and effort associated with learning firstprinciples dynamical models of the underlying physical system. An alternative approach is to employ learning algorithms to build blackbox models which rely only on realtime data from the sensors. Machine learning is widely used for regression and classification, but thus far datadriven models have not ...
Apex: Autonomous Vehicle Plan Verification And Execution, Matthew O'Kelly, Houssam Abbas, Sicun Gao, Shin'ichi Shiraishi, Shinpei Kato, Rahul Mangharam
Apex: Autonomous Vehicle Plan Verification And Execution, Matthew O'Kelly, Houssam Abbas, Sicun Gao, Shin'ichi Shiraishi, Shinpei Kato, Rahul Mangharam
RealTime and Embedded Systems Lab (mLAB)
Autonomous vehicles (AVs) have already driven millions of miles on public roads, but even the simplest scenarios have not been certified for safety. Current methodologies for the verification of AV's decision and control systems attempt to divorce the lower level, shortterm trajectory planning and trajectory tracking functions from the behavioral rulesbased framework that governs midterm actions. Such analysis is typically predicated on the discretization of the state space and has several limitations. First, it requires that a conservative buffer be added around obstacles such that many feasible plans are classified as unsafe. Second, the discretized controllers modeled in this ...
Topological Conditions For InNetwork Stabilization Of Dynamical Systems, Miroslav Pajic, Shreyas Sundaram, Rahul Mangharam, George Pappas
Topological Conditions For InNetwork Stabilization Of Dynamical Systems, Miroslav Pajic, Shreyas Sundaram, Rahul Mangharam, George Pappas
RealTime and Embedded Systems Lab (mLAB)
We study the problem of stabilizing a linear system over a wireless network using a simple innetwork computation method. Specifically, we study an architecture called the "Wireless Control Network'' (WCN), where each wireless node maintains a state, and periodically updates it as a linear combination of neighboring plant outputs and node states. This architecture has previously been shown to have low computational overhead and beneficial scheduling and compositionality properties. In this paper we characterize fundamental topological conditions to allow stabilization using such a scheme. To achieve this, we exploit the fact that the WCN scheme causes the network to act ...