Robustness Guided Temporal Logic Testing of Stochastic Cyber-Physical Systems

Research output: Patent

Abstract

A cyber-physical system is a system of collaborating computational elements that control physical entities. Most cyber-physical systems are safety-critical systems, such as control systems for aircraft, automobiles, and medical devices. As these systems become increasingly software-driven, errors become more difficult to detect and failures can be very expensive in both human lives and economic costs. A stochastic cyber-physical system is preprogrammed to respond to any environmental uncertainties (such as communication loss or tracking missiles) the system may encounter as it operates whatever device or vehicle the system is designed to control. Validation of stochastic cyber-physical systems are primarily conducted by statistical model checking techniques that lack a superior level of analytical precision. Researchers at Arizona State University have developed a technique for testing stochastic cyber-physical systems by determining the possible worst case scenarios. Applying the latest advancements in stochastic optimization, this technique consists of an algorithm that uses the ASU-developed MatLab toolbox, S-TaLiRo, to quantify the worst expected system behavior. These behaviors are retuned for debugging so the user can test and design a more robust system that is less likely to fail. Potential Applications Automatic Pilot Avionics Autonomous Automotive Systems Medical Monitoring Process Control Systems Robotics Smart Grid Technology Benefits and Advantages Less Work Reduces number of hours from initial design to market by specifying worst case scenarios and automating code generation. Lower Costs Reduces the need for physical prototypes. Robust Allows the user to create a more comprehensive CPS, reducing the likelihood of system failure within a guaranteed probability. Safety Less CPS failures means more human lives will potentially be saved. Download Original PDF For more information about the inventor(s) and their research, please see Dr. Georgios Fainekos' directory webpage
Original languageEnglish (US)
StatePublished - Jul 11 2013

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Temporal logic
Testing
Automatic pilots
Control systems
Patient monitoring
Avionics
Model checking
Missiles
Automobiles
Process control
Costs
Robotics
Computer systems
Aircraft
Cyber Physical System
Economics
Communication

Cite this

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title = "Robustness Guided Temporal Logic Testing of Stochastic Cyber-Physical Systems",
abstract = "A cyber-physical system is a system of collaborating computational elements that control physical entities. Most cyber-physical systems are safety-critical systems, such as control systems for aircraft, automobiles, and medical devices. As these systems become increasingly software-driven, errors become more difficult to detect and failures can be very expensive in both human lives and economic costs. A stochastic cyber-physical system is preprogrammed to respond to any environmental uncertainties (such as communication loss or tracking missiles) the system may encounter as it operates whatever device or vehicle the system is designed to control. Validation of stochastic cyber-physical systems are primarily conducted by statistical model checking techniques that lack a superior level of analytical precision. Researchers at Arizona State University have developed a technique for testing stochastic cyber-physical systems by determining the possible worst case scenarios. Applying the latest advancements in stochastic optimization, this technique consists of an algorithm that uses the ASU-developed MatLab toolbox, S-TaLiRo, to quantify the worst expected system behavior. These behaviors are retuned for debugging so the user can test and design a more robust system that is less likely to fail. Potential Applications Automatic Pilot Avionics Autonomous Automotive Systems Medical Monitoring Process Control Systems Robotics Smart Grid Technology Benefits and Advantages Less Work Reduces number of hours from initial design to market by specifying worst case scenarios and automating code generation. Lower Costs Reduces the need for physical prototypes. Robust Allows the user to create a more comprehensive CPS, reducing the likelihood of system failure within a guaranteed probability. Safety Less CPS failures means more human lives will potentially be saved. Download Original PDF For more information about the inventor(s) and their research, please see Dr. Georgios Fainekos' directory webpage",
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N2 - A cyber-physical system is a system of collaborating computational elements that control physical entities. Most cyber-physical systems are safety-critical systems, such as control systems for aircraft, automobiles, and medical devices. As these systems become increasingly software-driven, errors become more difficult to detect and failures can be very expensive in both human lives and economic costs. A stochastic cyber-physical system is preprogrammed to respond to any environmental uncertainties (such as communication loss or tracking missiles) the system may encounter as it operates whatever device or vehicle the system is designed to control. Validation of stochastic cyber-physical systems are primarily conducted by statistical model checking techniques that lack a superior level of analytical precision. Researchers at Arizona State University have developed a technique for testing stochastic cyber-physical systems by determining the possible worst case scenarios. Applying the latest advancements in stochastic optimization, this technique consists of an algorithm that uses the ASU-developed MatLab toolbox, S-TaLiRo, to quantify the worst expected system behavior. These behaviors are retuned for debugging so the user can test and design a more robust system that is less likely to fail. Potential Applications Automatic Pilot Avionics Autonomous Automotive Systems Medical Monitoring Process Control Systems Robotics Smart Grid Technology Benefits and Advantages Less Work Reduces number of hours from initial design to market by specifying worst case scenarios and automating code generation. Lower Costs Reduces the need for physical prototypes. Robust Allows the user to create a more comprehensive CPS, reducing the likelihood of system failure within a guaranteed probability. Safety Less CPS failures means more human lives will potentially be saved. Download Original PDF For more information about the inventor(s) and their research, please see Dr. Georgios Fainekos' directory webpage

AB - A cyber-physical system is a system of collaborating computational elements that control physical entities. Most cyber-physical systems are safety-critical systems, such as control systems for aircraft, automobiles, and medical devices. As these systems become increasingly software-driven, errors become more difficult to detect and failures can be very expensive in both human lives and economic costs. A stochastic cyber-physical system is preprogrammed to respond to any environmental uncertainties (such as communication loss or tracking missiles) the system may encounter as it operates whatever device or vehicle the system is designed to control. Validation of stochastic cyber-physical systems are primarily conducted by statistical model checking techniques that lack a superior level of analytical precision. Researchers at Arizona State University have developed a technique for testing stochastic cyber-physical systems by determining the possible worst case scenarios. Applying the latest advancements in stochastic optimization, this technique consists of an algorithm that uses the ASU-developed MatLab toolbox, S-TaLiRo, to quantify the worst expected system behavior. These behaviors are retuned for debugging so the user can test and design a more robust system that is less likely to fail. Potential Applications Automatic Pilot Avionics Autonomous Automotive Systems Medical Monitoring Process Control Systems Robotics Smart Grid Technology Benefits and Advantages Less Work Reduces number of hours from initial design to market by specifying worst case scenarios and automating code generation. Lower Costs Reduces the need for physical prototypes. Robust Allows the user to create a more comprehensive CPS, reducing the likelihood of system failure within a guaranteed probability. Safety Less CPS failures means more human lives will potentially be saved. Download Original PDF For more information about the inventor(s) and their research, please see Dr. Georgios Fainekos' directory webpage

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