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Articles 1 - 14 of 14

Full-Text Articles in Engineering

Synthesizing Stealthy Reprogramming Attacks On Cardiac Devices, Nicola Paoletti, Zhihao Jiang, Ariful Islam, Houssam Abbas, Rahul Mangharam, Shan Lin, Zachary Gruber, Scott A. Smolka Apr 2019

Synthesizing Stealthy Reprogramming Attacks On Cardiac Devices, Nicola Paoletti, Zhihao Jiang, Ariful Islam, Houssam Abbas, Rahul Mangharam, Shan Lin, Zachary Gruber, Scott A. Smolka

Real-Time and Embedded Systems Lab (mLAB)

An Implantable Cardioverter Defibrillator (ICD) is a medical device used for the detection of potentially fatal cardiac arrhythmias and their treatment through the delivery of electrical shocks intended to restore normal heart rhythm. An ICD reprogramming attack seeks to alter the device’s parameters to induce unnecessary therapy or prevent required therapy. In this paper, we present a formal approach for the synthesis of ICD reprogramming attacks that are both effective, i.e., lead to fundamental changes in the required therapy, and stealthy, i.e., are hard to detect. We focus on the discrimination algorithm underlying Boston Scientific devices (one ...


Technical Report: Anytime Computation And Control For Autonomous Systems, Yash Vardhan Pant, Houssam Abbas, Kartik Mohta, Rhudii A. Quaye, Truong X. Nghiem, Joseph Devietti, Rahul Mangharam Apr 2019

Technical Report: Anytime Computation And Control For Autonomous Systems, Yash Vardhan Pant, Houssam Abbas, Kartik Mohta, Rhudii A. Quaye, Truong X. Nghiem, Joseph Devietti, Rahul Mangharam

Real-Time and Embedded Systems Lab (mLAB)

The correct and timely completion of the sensing and action loop is of utmost importance in safety critical autonomous systems. A crucial part of the performance of this feedback control loop are the computation time and accuracy of the estimator which produces state estimates used by the controller. These state estimators, especially those used for localization, often use computationally expensive perception algorithms like visual object tracking. With on-board computers on autonomous robots being computationally limited, the computation time of a perception-based estimation algorithm can at times be high enough to result in poor control performance. In this work, we develop ...


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 ...


Fly-By-Logic: Control Of Multi-Drone Fleets With Temporal Logic Objectives, Yash Vardhan Pant, Houssam Abbas, Rhudii A. Quaye, Rahul Mangharam Mar 2018

Fly-By-Logic: Control Of Multi-Drone Fleets With Temporal Logic Objectives, Yash Vardhan Pant, Houssam Abbas, Rhudii A. Quaye, Rahul Mangharam

Real-Time and Embedded Systems Lab (mLAB)

The problem of safe planning and control for multi- drone systems across a variety of missions is of critical impor- tance, as the scope of tasks assigned to such systems increases. In this paper, we present an approach to solve this problem for multi-quadrotor missions. Given a mission expressed in Signal Temporal Logic (STL), our controller maximizes robustness to generate trajectories for the quadrotors that satisfy the STL spec- ification in continuous-time. We also show that the constraints on our optimization guarantees that these trajectories can be tracked nearly perfectly by lower level off-the-shelf position and attitude controllers. Our approach ...


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 ...


Smooth Operator: Control Using The Smooth Robustness Of Temporal Logic, Yash Vardhan Pant, Houssam Abbas, Rahul Mangharam Aug 2017

Smooth Operator: Control Using The Smooth Robustness Of Temporal Logic, Yash Vardhan Pant, Houssam Abbas, Rahul Mangharam

Real-Time and Embedded Systems Lab (mLAB)

Modern control systems, like controllers for swarms of quadrotors, must satisfy complex control objectives while withstanding a wide range of disturbances, from bugs in their software to attacks on their sensors and changes in their environments. These requirements go beyond stability and tracking, and involve temporal and sequencing constraints on system response to various events. This work formalizes the requirements as formulas in Metric Temporal Logic (MTL), and designs a controller that maximizes the robustness of the MTL formula. Formally, if the system satisfies the formula with robustness r, then any disturbance of size less than r cannot cause it ...


Technical Report: Control Using The Smooth Robustness Of Temporal Logic, Yash Vardhan Pant, Houssam Abbas, Rahul Mangharam Mar 2017

Technical Report: Control Using The Smooth Robustness Of Temporal Logic, Yash Vardhan Pant, Houssam Abbas, Rahul Mangharam

Real-Time and Embedded Systems Lab (mLAB)

Cyber-Physical Systems must withstand a wide range of errors, from bugs in their software to attacks on their physical sensors. Given a formal specification of their desired behavior in Metric Temporal Logic (MTL), the robust semantics of the specification provides a notion of system robustness that can be calculated directly on the output behavior of the system, without explicit reference to the various sources or models of the errors. The robustness of the MTL specification has been used both to verify the system offline (via robustness minimization) and to control the system online (to maximize its robustness over some horizon ...


Relaxed Decidability And The Robust Semantics Of Metric Temporal Logic, Houssam Abbas, Matthew O'Kelly, Rahul Mangharam Feb 2017

Relaxed Decidability And The Robust Semantics Of Metric Temporal Logic, Houssam Abbas, Matthew O'Kelly, Rahul Mangharam

Real-Time and Embedded Systems Lab (mLAB)

Relaxed notions of decidability widen the scope of automatic verification of hybrid systems. In quasi-decidability and $\delta$-decidability, the fundamental compromise is that if we are willing to accept a slight error in the algorithm's answer, or a slight restriction on the class of problems we verify, then it is possible to obtain practically useful answers. This paper explores the connections between relaxed decidability and the robust semantics of Metric Temporal Logic formulas. It establishes a formal equivalence between the robustness degree of MTL specifications, and the imprecision parameter $\delta$ used in $\delta$-decidability when it is used to ...


Tech Report: Robust Model Predictive Control For Non-Linear Systems With Input And State Constraints Via Feedback Linearization, Yash Vardhan Pant, Houssam Abbas, Rahul Mangharam Mar 2016

Tech Report: Robust Model Predictive Control For Non-Linear Systems With Input And State Constraints Via Feedback Linearization, Yash Vardhan Pant, Houssam Abbas, Rahul Mangharam

Real-Time and Embedded Systems Lab (mLAB)

Robust predictive control of non-linear systems under state estimation errors and input and state constraints is a challenging problem, and solutions to it have generally involved solving computationally hard non-linear optimizations. Feedback linearization has reduced the computational burden, but has not yet been solved for robust model predictive control under estimation errors and constraints. In this paper, we solve this problem of robust control of a non-linear system under bounded state estimation errors and input and state constraints using feedback linearization. We do so by developing robust constraints on the feedback linearized system such that the non-linear system respects its ...


Robust Model Predictive Control For Non-Linear Systems With Input And State Constraints Via Feedback Linearization, Yash Vardhan Pant, Houssam Abbas, Rahul Mangharam Jan 2016

Robust Model Predictive Control For Non-Linear Systems With Input And State Constraints Via Feedback Linearization, Yash Vardhan Pant, Houssam Abbas, Rahul Mangharam

Real-Time and Embedded Systems Lab (mLAB)

Robust predictive control of non-linear systems under state estimation errors and input and state constraints is a challenging problem, and solutions to it have generally involved solving computationally hard non-linear optimizations. Feedback linearization has reduced the computational burden, but has not yet been solved for robust model predictive control under estimation errors and constraints. In this paper, we solve this problem of robust control of a non-linear system under bounded state estimation errors and input and state constraints using feedback linearization. We do so by developing robust constraints on the feedback linearized system such that the non-linear system respects its ...


Co-Design Of Anytime Computation And Robust Control (Supplemental), Yash Vardhan Pant, Kartik Mohta, Houssam Abbas, Truong X Nghiem, Joesph Deveitti, Rahul Mangharam May 2015

Co-Design Of Anytime Computation And Robust Control (Supplemental), Yash Vardhan Pant, Kartik Mohta, Houssam Abbas, Truong X Nghiem, Joesph Deveitti, Rahul Mangharam

Real-Time and Embedded Systems Lab (mLAB)

No abstract provided.


Co-Design Of Anytime Computation And Robust Control, Yash Vardhan Pant, Kartik Mohta, Houssam Abbas, Truong Nghiem, Joesph Deveitti, Rahul Mangharam Jan 2015

Co-Design Of Anytime Computation And Robust Control, Yash Vardhan Pant, Kartik Mohta, Houssam Abbas, Truong Nghiem, Joesph Deveitti, Rahul Mangharam

Real-Time and Embedded Systems Lab (mLAB)

No abstract provided.


Requirement-Guided Model Refinement, Zhihao Jiang, Pieter Mosterman, Rahul Mangharam Dec 2014

Requirement-Guided Model Refinement, Zhihao Jiang, Pieter Mosterman, Rahul Mangharam

Real-Time and Embedded Systems Lab (mLAB)

Medical device is a typical Cyber-Physical System and ensuring the safety and efficacy of the device requires closed-loop verification. Currently closed-loop verifications of medical devices are performed in the form of clinical trials in which the devices are tested on the patients.