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1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS

Associate Professor in Software Engineering à IEEE Software
26 Feb 2016
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
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1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
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1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
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1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
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1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
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1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
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1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
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1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
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1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
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1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
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1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
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1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
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1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
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1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
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1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
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1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS
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  1. Fon any information please contact Alessandro D’Innocenzo – alessandro.dinnocenzo@univaq.it - or Henry Muccini - henry.muccini@univaq.it 1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS TUESDAY 26, JANUARY 2016, 2:00 PM MEETING ROOM 2.3, II FLOOR, COPPITO 1 UNIVERSITY OF L’AQUILA, ITALY PROGRAM 14:00 - Alessandro D’Innocenzo & Henry Muccini - Welcome & Introduction to CPS 14:20 - Alessandro D’Innocenzo - Modeling and Co-design of Control Tasks over Wireless Networking Protocols: State of the Art and Challenges 14:40 - Giordano Pola – Formal methods for analysis and control of CPS 15:00 - Elena De Santis - Safe Communication in Power Systems: application to a DC microgrid control - Safe Human-Inspired Model for Vehicle Control 15:20 – Henry Muccini – Architecting (Self-Adaptive) Cyber-Physical Systems: a View on the State of the Art 15:40 - Luigi Pomante: Electronic Design Automation & Embedded Systems Development 16:00 - Stefania Costantini - Agent-based hybrid architecture for Smart Cyber-Physical Systems and applications to eHealth 16:20 - Discussion
  2. Alessandro D’Innocenzo, Henry Muccini alessandro.dinnocenzo@univaq.it henry.muccini@univaq.it DISIM Dept. of Information Engineering, Computer Science and Mathematics University of L’Aquila, Italy DEWS Centre of Excellence on Design Methodologies of Embedded Controllers, Wireless Interconnect and Systems-on-chip - University of L’Aquila, Italy
  3. SEA Group The Next Computing Revolution Mainframe computing (60’s – 70’s) Large computers to execute big data processing applications Desktop computing & Internet (80’s – 90’s) One computer at every desk to do business/personal activities Ubiquitous computing (00’s) Numerous computing devices in every place/person Millions for desktops vs. billions for embedded processors Cyber Physical Systems (10’s)
  4. SEA Group What are Cyber Physical Systems? Cyber-Physical Systems (CPS) as ``engineered systems that are built from, and depend upon, the seamless integration of computational and physical components” [NSF12] Cyber-Physical Systems (CPS) are integrations of computation with physical processes. Embedded computers and networks monitor and control the physical processes, usually with feedback loops where physical processes affect computations and vice versa [Lee08] A cyber-physical system (CPS) is a system of collaborating computational elements controlling physical entities [Wikipedia].
  5. SEA Group HW/SW component HW/SW component HW/SW component HW/SW component HW/SW component Monitor and control Affect Feedback loop Collaborate
  6. SEA Group Different names for same things… Cyberphysical Systems (CPS), Networked Embedded Systems, SCADA, Swarm Robotics, Drone Sensor Networks, Internet of Things (IoT), Wireless Sensor Networks (WSN),
  7. SEA Group Main characteristics - Networked embedded components - Feedback loop - Adaptable, re-configurable, dynamic - Distributed control
  8. SEA Group CPS versus Embedded Systems CPS represents an evolution of embedded systems, where components are immersed in and interacting with the physical world CPS has to satisfy new requirements, such as continuous evolution and adaptability, due to the computational complexity, distribution and system adaptability of those systems.
  9. SEA Group Example #1 (taken from Luca Mottola slides)
  10. SEA Group Example #1 (taken from Luca Mottola slides)
  11. SEA Group Example #2: self-driving cars
  12. SEA Group Example #3: smart buildings
  13. INCIPICT SER2: Building automation systems: Motivations Physical modeling, automatic control, communication: Cyber-Physical Systems
  14. Rule Based DR Model Based DR Data-Driven DR Building automation systems: SoA Courtesy of Madhur Behl
  15. SEA Group CPS versus Networked Systems CPS represents an evolution of networked control systems, where physical systems and controllers interact via a communication system CPS inherit from NCS challenges on distributed control and dynamic reconfiguration
  16. Networked Control Systems Plant u y x • Let a plant model be given by input/output/internal variables and differential/difference equations, e.g.: ‫ݔ‬ ݇ + 1 = ‫ݔܣ‬ ݇ + ‫ݑܤ‬ ݇ , ‫ݕ‬ ݇ = ‫)݇(ݔܥ‬
  17. Networked Control Systems Plant u y x • Let a plant model be given by input/output/internal variables and differential/difference equations, e.g.: ‫ݔ‬ ݇ + 1 = ‫ݔܣ‬ ݇ + ‫ݑܤ‬ ݇ , ‫ݕ‬ ݇ = ‫)݇(ݔܥ‬ • Let some specifications be given on the desired behavior of the variables, e.g. stability or some temporal logic formula
  18. Networked Control Systems PlantController u y x • Let a plant model be given by input/output/internal variables and differential/difference equations, e.g.: ‫ݔ‬ ݇ + 1 = ‫ݔܣ‬ ݇ + ‫ݑܤ‬ ݇ , ‫ݕ‬ ݇ = ‫)݇(ݔܥ‬ • Let some specifications be given on the desired behavior of the variables, e.g. stability or some temporal logic formula • Design a controller such that the closed-loop interconnection satisfies the specifications, e.g. ℎ ݇ + 1 = ‫ܧ‬ℎ ݇ + ‫ݕܨ‬ ݇ , u ݇ = ‫ܩ‬ℎ(݇)
  19. Networked Control Systems PlantController • Let a plant model be given by input/output/internal variables and differential/difference equations, e.g.: ‫ݔ‬ ݇ + 1 = ‫ݔܣ‬ ݇ + ‫ݑܤ‬ ݇ , ‫ݕ‬ ݇ = ‫)݇(ݔܥ‬ • Let some specifications be given on the desired behavior of the variables, e.g. stability or some temporal logic formula • Design a controller such that the closed-loop interconnection satisfies the specifications, e.g. ℎ ݇ + 1 = ‫ܧ‬ℎ ݇ + ‫ݕܨ‬ ݇ , u ݇ = ‫ܩ‬ℎ ݇ • What if plant and controller exchange data via a communication network? R. Alur, A. D'Innocenzo, K.H. Johansson, G.J. Pappas, G. Weiss. Compositional Modeling and Analysis of Multi-Hop Control Networks. IEEE Transactions on Automatic Control, Special Issue on Wireless Sensor and Actuator Networks, full paper, 56(10):2345-2357, 2011. u y xComm. Network
  20. SEA Group Bibliography [NSF12] National Science Foundation, Cyber-Physical Systems Program Solicitation NSF 13-502, October 2012 [Lee08] Edward A. Lee. Cyber Physical Systems: Design Challenges.Technical Report No. UCB/EECS-2008-8, January 23, 2008
  21. SEA Group
  22. ​Modeling and Co-design of Control Tasks over Wireless Networking Protocols: State of the Art and Challenges Alessandro D’Innocenzo 1st DISIM Workshop on Engineering Cyber Physical Systems January 26, 2016 – University of L’Aquila
  23. Objective 1: Robust & secure design of control tasks over wireless communication protocols Objective 2: Co-simulation and emulation of control algorithms, communication protocols and physical systems • Formal compositional interfaces between control algorithms and wireless communication protocols • Quantify impact of wireless networking on control performance • Robustness with respect to packet losses and delays • Resilience with respect to failures and malicious intrusions • Formal verification tools and co-simulation environments
  24. Goal: to develop novel methods for co-design of control algorithms and communication protocol configuration Method: Interdisciplinary research across the “3C”: control theory, computer science and communication theory Output: novel methods that improve performance and security of technological solutions for wireless automation systems Contact info: alessandro.dinnocenzo@univaq.it
  25. Control task Plant u y x • Let a plant model be given by input/output/internal variables and differential/difference equations, e.g.: 𝑥 𝑘 + 1 = 𝐴𝑥 𝑘 + 𝐵𝑢 𝑘 , 𝑦 𝑘 = 𝐶𝑥(𝑘)
  26. Control task Plant u y x • Let a plant model be given by input/output/internal variables and differential/difference equations, e.g.: 𝑥 𝑘 + 1 = 𝐴𝑥 𝑘 + 𝐵𝑢 𝑘 , 𝑦 𝑘 = 𝐶𝑥(𝑘) • Let some specifications be given on the desired behavior of the variables, e.g. stabilityor some temporal logicformula
  27. Control task PlantController u y x • Let a plant model be given by input/output/internal variables and differential/difference equations, e.g.: 𝑥 𝑘 + 1 = 𝐴𝑥 𝑘 + 𝐵𝑢 𝑘 , 𝑦 𝑘 = 𝐶𝑥(𝑘) • Let some specifications be given on the desired behavior of the variables, e.g. stabilityor some temporal logicformula • Design a controller such that the closed-loop interconnectionsatisfies the specifications, e.g. ℎ 𝑘 + 1 = 𝐸ℎ 𝑘 + 𝐹𝑦 𝑘 , u 𝑘 = 𝐺ℎ(𝑘)
  28. Control task PlantController • Let a plant model be given by input/output/internal variables and differential/difference equations, e.g.: 𝑥 𝑘 + 1 = 𝐴𝑥 𝑘 + 𝐵𝑢 𝑘 , 𝑦 𝑘 = 𝐶𝑥(𝑘) • Let some specifications be given on the desired behavior of the variables, e.g. stabilityor some temporal logicformula • Design a controller such that the closed-loop interconnectionsatisfies the specifications, e.g. ℎ 𝑘 + 1 = 𝐸ℎ 𝑘 + 𝐹𝑦 𝑘 , u 𝑘 = 𝐺ℎ 𝑘 • What if plant and controller exchange data via a wireless network? R. Alur, A. D'Innocenzo, K.H. Johansson, G.J. Pappas, G. Weiss. Compositional Modeling and Analysis of Multi-Hop Control Networks. IEEE Transactions on Automatic Control, Special Issue on Wireless Sensor and Actuator Networks, full paper, 56(10):2345-2357, 2011. u y xWireless Network
  29. Challenges with Wired Control Networks Wires are expensive • Wires as well as installationcosts • Wire/connector wear and tear Lack of flexibility • Wires constrain sensor/actuator mobility • Limited reconfigurationoptions Restricted control architectures • Centralizedcontrol paradigm
  30. Paradigm shift towards wireless control architectures “Removing cables undoubtedly saves cost, but often the real cost gains lie in the radically different design approach that wireless solutions permit. […] In order to fully benefit from wireless technologies, a rethink of existing automation concepts and the complete design and functionality of an application is required.” Jan-Erik Frey, R&D Manager ABB
  31. Wireless Control Network A collection of cooperating algorithms (controllers) designed to achieve a set of common goals, aided by interactions with the environment through distributed measurements (sensors) and actions (actuators) exchanged via a wireless communication network
  32. Wireless Control Network A collection of cooperating algorithms (controllers) designed to achieve a set of common goals, aided by interactions with the environment through distributed measurements (sensors) and actions (actuators) exchanged via a wireless communication network
  33. Applications of Wireless Control Networks Industrial automation EnvironmentalMonitoring, Disaster Recovery and Preventive Conservation Supply Chain and Asset Management Physical Security and Control
  34. Opportunities vs challenges with Wireless Control Networks Lower costs, easier installation • Suitable for emerging markets Broadens scope of sensing and control • Easier to sense/monitor/actuate: opens new application domains Compositionality • Enables system evolution via composable control loops Runtime adaptation and reconfiguration • Control can be maintained in response to failures and malicious attacks Complexity • Systems designers and programmers need suitable abstractions to hide the complexity from wireless devices and communication protocols Reliability • Need for robust and predictable behavior despite wireless non-idealities Security • Wireless technology is vulnerable: security mechanisms for control loops Take into account communication protocol behavior!
  35. ISO/OSI model for (wireless) communication protocols Application Session Presentation Transport Network Data/Link Physical Application Session Presentation Transport Network Data/Link Physical Wireless link))) ((( • Open systems interconnection (OSI) model separates functional elements of a network into seven layers Host A Host B
  36. ISO/OSI model for (wireless) communication protocols Application Session Presentation Transport Network Data/Link Physical Application Session Presentation Transport Network Data/Link Physical Wireless link))) ((( Interference, data losses, delays, limited energy, channel capacity, failures, malicious intrusions Coding, modulation, tx power Scheduling, access to the wireless channel Routing strategy • Open systems interconnection (OSI) model separates functional elements of a network into seven layers • OSI model has allowed refinement of each layer independently Skype, youTube… TCP, UDP Host A Host B
  37. ISO/OSI model for (wireless) communication protocols Application Session Presentation Transport Network Data/Link Physical Application Session Presentation Transport Network Data/Link Physical Wireless link))) ((( • Open systems interconnection (OSI) model separates functional elements of a network into seven layers • OSI model has allowed refinement of each layer independently • Each layer only talks with the corresponding layer…by exchanging packets with the layers above & below Host A Host B
  38. Classical control loop 𝑢 𝑘 = 𝑓(𝑦 𝑘 ) Application Session Presentation Transport Network Data/Link Physical Application Session Presentation Transport Network Data/Link Physical Wireless link))) ((( S1 • Communication stack and medium is transparent to the control algorithm A1 A2 Robust and Fault-tolerant Control 𝑢5 𝑘 𝑢6 𝑘 y 𝑘 𝑢5 𝑘 𝑢6 𝑘 y 𝑘
  39. Plant control law Control loop over a wireless network 𝐮 𝐤 = 𝐟(𝐲 𝐤 ) Session Presentation Transport Network Data/Link Physical Sensing/actuation Session Presentation Transport Network Data/Link Physical Wireless link))) ((( • Sensing and actuation data are relayed via the protocol stack layers S1A1 A2
  40. Control loops over a wireless network A1 A2 S1 𝐮 𝐤 = 𝐟(𝐲 𝐤 ) Session Presentation Transport Network Data/Link Physical Sensing/actuation Session Presentation Transport Network Data/Link Physical Wireless link))) ((( Plant control law • Sensing and actuation data are relayed via the protocol stack layers • Several feedback control mechanisms within separate communication layers TCP congestion control Routing control Medium access control Power, coding & modulation control Intra-layer control loops
  41. Control loops over a real wireless network Wireless network
  42. Control loops over a real wireless network Wireless network Borderline between control over network and control of network disappears M.C. Escher, Relativity Lithograph, 1953
  43. Control loops over a real wireless network Wireless network Borderline between control over network and control of network disappears M.C. Escher, Relativity Lithograph, 1953 Different perspectives in terms of • Time-scales • Mathematical setting • Performance metrics • Constraints & non-idealities
  44. Handle complexity of CPS via hybrid systems theory J.Lygeros,S.Sastry,C.J.Tomlin. A game theoretic approach to controller design for hybrid systems. In Proc. Of IEEE 88(7):949-970, July 2000 • Discrete Variables: – Heater off: q0 – Heater on: q1 • Continuous Variables: – Room temperature : x • Transitions: – Turn heater ON when the temperature is smaller than 70 degrees: x≤70. – Turn heater OFF when the temperature is greater than 80 degrees: x≥80. • Analysis and control of hybrid systems via formal methods: – Discretize state space: Pola et al. […] – Discretize trajectories: Yi Deng, A. D'Innocenzo, M. D. Di Benedetto, S. Di Gennaro, A. A. Julius. Verification of Hybrid Automata Diagnosability with Measurement Uncertainty. IEEE Transactions on Automatic Control
  45. Challenge: Co-design the control algorithm and the communication protocol Controller Application Session Presentation Transport Network Data/Link Physical Handle complexity of CPS via tailored modeling and design
  46. Co-design over time-triggered communication protocols Challenge: Co-design the control algorithm and the communication protocol (scheduling, routing and control) Controller Application Session Presentation Transport Network Data/Link Physical
  47. WirelessHART MAC (scheduling) and Network (routing) layers § Time-triggered access to the channel § Time divided in periodic frames § Each frame divided in Π time slots of duration Δ
  48. 27 WirelessHART MAC (scheduling) and Network (routing) layers § Time-triggered access to the channel § Time divided in periodic frames § Each frame divided in Π time slots of duration Δ
  49. 28 WirelessHART MAC (scheduling) and Network (routing) layers § Time-triggered access to the channel § Time divided in periodic frames § Each frame divided in Π time slots of duration Δ § Enables redundancy in data routing
  50. 29 WirelessHART MAC (scheduling) and Network (routing) layers § Time-triggered access to the channel § Time divided in periodic frames § Each frame divided in Π time slots of duration Δ § Enables redundancy in data routing
  51. 30 WirelessHART MAC (scheduling) and Network (routing) layers § Time-triggered access to the channel § Time divided in periodic frames § Each frame divided in Π time slots of duration Δ § Enables redundancy in data routing
  52. 31 WirelessHART MAC (scheduling) and Network (routing) layers § Time-triggered access to the channel § Time divided in periodic frames § Each frame divided in Π time slots of duration Δ § Enables redundancy in data routing § Scheduling must guarantee relay via multiple paths
  53. 32 WirelessHART MAC (scheduling) and Network (routing) layers § Time-triggered access to the channel § Time divided in periodic frames § Each frame divided in Π time slots of duration Δ § Enables redundancy in data routing § Scheduling must guarantee relay via multiple paths
  54. 33 WirelessHART MAC (scheduling) and Network (routing) layers § Time-triggered access to the channel § Time divided in periodic frames § Each frame divided in Π time slots of duration Δ § Enables redundancy in data routing § Scheduling must guarantee relay via multiple paths
  55. 34 WirelessHART MAC (scheduling) and Network (routing) layers § Time-triggered access to the channel § Time divided in periodic frames § Each frame divided in Π time slots of duration Δ § Enables redundancy in data routing § Scheduling must guarantee relay via multiple paths Protocols designed for “slow” control tasks: exploit redundancy to use it on “fast” control tasks
  56. Redundancy in data routing… § …makes system tolerant to long- term link failures § …enables detection and isolation of failures and malicious attacks § …makes system robust to short-term link failures (e.g. packet losses)
  57. Redundancy in data routing… Close a control loop investigating two routing strategies: 1. Single-path dynamic routing: switching behavior due to dynamic routing 2. Multi-path static routing:algorithms to merge redundant data § …makes system tolerant to long- term link failures § …enables detection and isolation of failures and malicious attacks § …makes system robust to short-term link failures (e.g. packet losses)
  58. Redundancy in data routing… § …makes system tolerant to long- term link failures § …enables detection and isolation of failures and malicious attacks § …makes system robust to short-term link failures (e.g. packet losses) Close a control loop investigating two routing strategies: 1. Single-path dynamic routing: switching behavior due to dynamic routing 2. Multi-path static routing:algorithms to merge redundant data
  59. Wireless control networks as switching systems 𝐾(𝑡)
  60. Wireless control networks as switching systems 𝑡 𝐾(𝑡)
  61. Wireless control networks as switching systems t+1 𝐾(𝑡)
  62. Wireless control networks as switching systems t+… 𝐾(𝑡) Different paths are associated with different delays.
  63. Wireless control networks as switching systems 𝑡+… 𝐾(𝑡) 𝐴 = 𝐴I 𝐵I 0 0 0 𝐼 ⋮ ⋮ ⋮ ⋯ 0 0 ⋯ 0 0 ⋱ ⋮ ⋮ 0 0 0 0 0 0 0 0 0 ⋯ 𝐼 0 ⋯ 0 𝐼 ⋯ 0 0 𝐵 𝜎 𝑡 = 𝐵𝛿Q R ,S 𝐼𝛿Q R ,5 ⋮ 𝐼𝛿Q R ,TU6 𝐼𝛿Q R ,TU5 𝐼𝛿Q R ,T Different paths are associated with different delays. Mathematical model: 𝑥 𝑡 + 1 = 𝐴𝑥 𝑡 + 𝐵 𝜎 𝑡 𝑣 𝑡 , 𝑡 ∈ ℕ, where 𝑥 𝑡 is the plant and network state, 𝜎 𝑡 ∈ Σ depends on routing/scheduling. The switching signal is considered as a disturbance.
  64. Wireless control networks as switching systems 𝑡+… Problem: Design a controller 𝐾(𝑡) s.t. the closed loop system is asymptotically stable. Given a state-feedback staticcontroller 𝐾(𝑡), the closed loop systems is asymptotically stable iff the Joint Spectral Radius of 𝐴 + 𝐵 𝜎 𝑡 𝐾 𝑡 Q R ∈Z is smaller than 1. Insights: Switching systems analysis and design is a crowded research area: • Leverage special structure of matrices 𝐴 and 𝐵 𝜎 𝑡 to provide tailored results that outperform classical results on general switching systems 𝐾(𝑡) Different paths are associated with different delays. Mathematical model: 𝑥 𝑡 + 1 = 𝐴𝑥 𝑡 + 𝐵 𝜎 𝑡 𝑣 𝑡 , 𝑡 ∈ ℕ, where 𝑥 𝑡 is the plant and network state, 𝜎 𝑡 ∈ Σ depends on routing/scheduling. The switching signal is considered as a disturbance.
  65. Wireless control networks as switching systems R. M. Jungers, A. D'Innocenzo, M. D. Di Benedetto. Modeling, analysis and design of linear systems with switching delays. IEEE Transactions on Automatic Control, to appear. A. Cicone, A. D'Innocenzo, N. Guglielmi, L. Laglia. A sub-optimal solution for optimal control of linear systems with unmeasurable switching delays. 54th IEEE Conference on Decision and Control, Osaka, Japan, December 15-18, 2015. R. M. Jungers, A. D'Innocenzo, M. D. Di Benedetto. Further results on controllability of linear systems with switching delays. 9th IFAC World Congress, Cape Town, South Africa, August 24-29, 2014. R. M. Jungers, A. D'Innocenzo, M. D. Di Benedetto. How to control Linear Systems with switching delays. 13th European Control Conference (ECC14), Strasbourg, France, June 24-27, 2014. R.M. Jungers, A. D'Innocenzo, M.D. Di Benedetto. Feedback stabilization of dynamical systems with switched delays. 51st IEEE Conference on Decision and Control, Maui, Hawaii, December 10-13 2012. 𝐾(𝑡)
  66. Redundancy in data routing… Close a control loop investigating two routing strategies: 1. Single-path dynamic routing:take into account switching behavior due to dynamic routing 2. Multi-path static routing:take into account algorithms to merge redundant data § …makes system tolerant to long- term link failures § …enables detection and isolation of failures and malicious attacks § …makes system robust to short-term link failures (e.g. packet losses)
  67. Multi-path static routing § …makes system tolerant to long- term link failures § …enables detection and isolation of failures and malicious attacks § …makes system robust to short-term link failures (e.g. packet losses) Investigate algorithms to merge redundant data: • Objective: stabilize the closed-loop system • Best strategy: keep most recent packet vs. compute combination? • Different paths are associated with different delays • Not a trivial question, best strategy from the point of view of stability strongly depends on plant and network: need for a control-theoretic approach
  68. Multi-path static routing § …makes system tolerant to long- term link failures § …enables detection and isolation of failures and malicious attacks § …makes system robust to short-term link failures (e.g. packet losses) Investigate algorithms to merge redundant data: • Objective: stabilize the closed-loop system • Best strategy: keep most recent packet vs. compute combination? • Different paths are associated with different delays • Not a trivial question, best strategy from the point of view of stability strongly depends on plant and network: need for a control-theoretic approach
  69. Syntax: § Linear plant 𝒫 = (𝐴, 𝐵, 𝐶) MIMO MCN model
  70. Syntax: § Linear plant § Weight function 𝑊 determines data processing through the network - reminiscent of network coding 𝐺ℛ = 𝑉ℛ, 𝐸ℛ, 𝑊ℛ 𝑊ℛ_ : 𝐸ℛ → ℝ 𝑖 = 1, ⋯, 𝑚 𝒫 = (𝐴, 𝐵, 𝐶) 𝐺 𝒪 = 𝑉𝒪, 𝐸 𝒪,𝑊𝒪 𝑊𝒪_ : 𝐸 𝒪 → ℝ 𝑖 = 1, ⋯, 𝑙 MIMO MCN model
  71. Syntax: § Linear plant § Weight function 𝑊 determines data processing through the network - reminiscent of network coding § Communication scheduling 𝜂 assigns transmission of nodes 𝐺ℛ = 𝑉ℛ, 𝐸ℛ, 𝑊ℛ 𝑊ℛ_ : 𝐸ℛ → ℝ 𝑖 = 1, ⋯, 𝑚 𝜂ℛ_ : 1, … , Π → 2jℛ 𝐺 𝒪 = 𝑉𝒪, 𝐸 𝒪,𝑊𝒪 𝑊𝒪_ : 𝐸 𝒪 → ℝ 𝑖 = 1, ⋯, 𝑙 𝜂 𝒪_ : 1, … , Π → 2j 𝒪 𝒫 = (𝐴, 𝐵, 𝐶) MIMO MCN model
  72. Syntax: § Linear plant § Weight function 𝑊 determines data processing through the network - reminiscent of network coding § Communication scheduling 𝜂 assigns transmission of nodes § Model at time scale of frames instead of time-slots (no switching behavior) 𝑇 = ΠΔ 𝐺ℛ = 𝑉ℛ, 𝐸ℛ, 𝑊ℛ 𝑊ℛ_ : 𝐸ℛ → ℝ 𝑖 = 1, ⋯, 𝑚 𝜂ℛ_ : 1, … , Π → 2jℛ 𝐺 𝒪 = 𝑉𝒪, 𝐸 𝒪,𝑊𝒪 𝑊𝒪_ : 𝐸 𝒪 → ℝ 𝑖 = 1, ⋯, 𝑙 𝜂 𝒪_ : 1, … , Π → 2j 𝒪 𝒫 = (𝐴, 𝐵, 𝐶) MIMO MCN model
  73. Resilient control 𝐹 set of all configurations of links subject to a failureor a malicious intrusion
  74. Resilient control 𝐹 set of all configurations of links subject to a failureor a malicious intrusion
  75. Resilient control 𝐹 set of all configurations of links subject to a failureor a malicious intrusion
  76. Resilient control Mf 𝐹 set of all configurations of links subject to a failureor a malicious intrusion
  77. Resilient control Mf 𝐹 set of all configurations of links subject to a failureor a malicious intrusion
  78. Resilient control Mf 𝐹 set of all configurations of links subject to a failureor a malicious intrusion § Benefit: Do not reconfigure the whole network (i.e. scheduling and routing) when a failure occurs: instead,onlyreconfigure neighbors offaultynodes § Benefit: Do not add complexity to local communication to detect faulty or malicious nodes:instead,use plant dynamics and path redundancy § Technical challenge: Exploit graph theory and control-theoretic approaches for model-based failure detection and isolation
  79. BANK OF LUENBERGER OBSERVERS 𝑓l 𝑘𝑇 = [𝑓l5 𝑘𝑇 , 𝑓l6 𝑘𝑇 ,… , 𝑓l|o| 𝑘𝑇 ] Observer-based diagonal FDI problem
  80. BANK OF LUENBERGER OBSERVERS 𝑓l 𝑘𝑇 = [𝑓l5 𝑘𝑇 , 𝑓l6 𝑘𝑇 ,… , 𝑓l|o| 𝑘𝑇 ] Observer-based diagonal FDI problem
  81. BANK OF LUENBERGER OBSERVERS 𝑓l 𝑘𝑇 = [𝑓l5 𝑘𝑇 , 𝑓l6 𝑘𝑇 ,… , 𝑓l|o| 𝑘𝑇 ] Observer-based diagonal FDI problem
  82. BANK OF LUENBERGER OBSERVERS 𝑓l 𝑘𝑇 = [𝑓l5 𝑘𝑇 , 𝑓l6 𝑘𝑇 ,… , 𝑓l|o| 𝑘𝑇 ] Observer-based diagonal FDI problem Derive a common mathematical model for network topology (graph) and plant (LTI system): exploit structured systems theory that translates LTI system into a graph
  83. Resilient control A. D'Innocenzo, F. Smarra, M.D. Di Benedetto. Fault Tolerant Control of MIMO Multi-Hop Control Networks. Automatica, full paper, to appear. A. D'Innocenzo, F. Smarra, M. D. Di Benedetto. Further results on fault detection and isolation of malicious nodes in Multi-hop Control Networks. 14th European Control Conference (ECC 2015), Linz, Austria, July 15- 17, 2015. Best application paper award. M.D. Di Benedetto, A. D'Innocenzo, F. Smarra. Fault-tolerant control of a wireless HVAC control system. ISCCSP2014, 2014 A. D'Innocenzo, M.D. Di Benedetto, F. Smarra. Fault detection and isolation of malicious nodes in MIMO Multi-hop Control Networks. 52nd IEEE CDC, 2013 F. Smarra, A. D'Innocenzo, M.D. Di Benedetto. Fault Tolerant Stabilizability of MIMO Multi-Hop Control Networks. 3rd IFAC NecSys 2012 A. D'Innocenzo, M.D. Di Benedetto, E. Serra. Fault Tolerant Control of Multi-Hop Control Networks. IEEE Transactions on Automatic Control, 58(6):1377-1389, 2013.
  84. Challenges in Wireless Control Networks Modeling • Formal interfaces between control algorithms and wireless communication protocols • Compositional models for scalable analysis and design of multiple control loops Analysis • Quantify impact of wireless networking on control performance Design • Controller design incorporating wireless network properties • Control-network co-design Robustness • Robust with respect to packet losses and delays • Tolerant with respect to failures and malicious intrusions – CPS Security (SafeCOP) Tools • Formal verification and automatic (co-)design of safe & secure WCN (SafeCOP) • Co-simulation of control algorithms, communication protocols and physical systems Experimental set-up • WirelessHART laboratory (INCIPICT + SafeCOP) • Building automation laboratory (INCIPICT)
  85. Formal Methods for the Analysis and Control of Cyber-Physical Systems Giordano Pola Department of Information Engineering, Computer Science and Mathematics, Center of Excellence DEWS, University of L’ Aquila, Italy giordano.pola@univaq.it
  86. At DISIM & DEWS:  Marika Di Benedetto  Pierdomenico Pepe  Elena De Santis  Costanzo Manes Outside:  Paulo Tabuada (UCLA, USA)  Karl Henrik Johansson (KTH, Sweden)  Arjan J. van der Schaft (University of Groningen, The Netherlands)  Antoine Girard (Universite’ Joseph Fourier, France)  Alessandro Borri (IASI-CNR, Italy)  Majid Zamani (TU Munich, The Netherlands)  Manuel Mazo (TU Delft, The Netherlands) Acknowledgments: 01/13 Collaborations
  87. 02/13 http://CyberPhysicalSystems.org Cyber Physical Systems (CPS) – a concept map
  88. 02/13 http://CyberPhysicalSystems.org Cyber Physical Systems (CPS) – a concept map
  89. 03/13 Network of plants Pi and computing units Ci communicating via non-ideal communication infrastructures Our model of CPS P1 P2 PN C1 C2 CN
  90. Our model of CPS 04/13 P1 P2 PN C1 C2 CN Plants: nonlinear control systems with possible disturbances and time-varying (states and inputs) delays dx(t) / dt = f (x(t),x(t-x(t)),u(t-u(t)),d(t)) :Pi
  91. Our model of CPS 05/13 P1 P2 PN C1 C2 CN Computing Units: Labelled transition systems T = (Q, Q0, L, ,O,H) :Ci
  92. Our model of CPS 06/13 Non-idealities in communication infrastructures:  Quantization errors  Bounded time-varying network access times  Bounded time-varying communication delays  Limited bandwidth  Bounded number of packet losses P1 P2 PN C1 C2 CN :
  93. 07/13 Goals:  Synthesis of correct-by-design embedded control software enforcing complex specifications  Detection of faults and/or criticalities in safety-critical CPS Our model of CPS P1 P2 PN C1 C2 CN
  94. Approach based on a three phases process: #1. construct the finite/symbolic model T of the plant system  #2. design a finite/symbolic controller C that solves the specification S for T #3. design a controller C’ for  on the basis of C Advantages:  Integration of software and hardware constraints in the control design of purely continuous processes  Use of computer science techniques to address complex logic specifications Correct-by-design embedded control software Symbolic domain Physical domain Plant: Continuous or Hybrid system Symbolic model Finite controllerSoftware & hardware Hybrid controller 08/13
  95. stable control systems [Automatica-2008] stable switched systems [IEEE-TAC-2010] stable time-delay systems [SCL-2010] stable time-varying delay systems [IJRNC-2014] [IJC-2012] unstable control systems [IEEE-TAC-2012] efficient control algorithms [IEEE-TAC-2012] approximate bisimulation [Girard & Pappas,IEEE-TAC-2007] incremental stability [Angeli,IEEE-TAC-2002] networked control systems [HSCC-2012] [IEEE-CDC-2012] [ERCIM News ‘97] [IEEE-TAC-2016 ?] Research at DEWS (IAB meeting 2014) PWA systems [IEEE-TAC-2014] networks of control systems [IEEE-ACC-2014] [IEEE-TAC-2016 ?] decentralized symbolic control & application to vehicle platooning [NecSys 2013] stable control systems with disturbances [SIAM-2009] 09/13
  96. #1. Construct the symbolic model T of the plant system  Done: 1.1 CPS with one plant and one computing unit communicating via nonideal communication infrastructure [Borri et al; HSCC-2012], [Liu et al.; HSCC-2014], [Zamani et al; IEEE-CDC-2015],] 1.2 CPS with multiple plants and computing units communicating via ideal communication infrastructure [Tazaki et al.; HSCC-2008], [Pola et al.; IEEE-TAC-2016 ?] To be done: 1.1 + 1.2 = ?  Incremental stability notions for CPS  Symbolic models for CPS with multiple plants and computing units communicating via nonideal communication infrastructure Correct-by-design embedded control software 10/13
  97. #2. Design a symbolic controller C that solves the specification S for T Done: [Borri et al; HSCC-2012]  Model: CPS with one plant and one computing unit communicating via nonideal communication infrastructure  Specifications: non-deterministic transition systems To be done:  Extension to symbolic control design with specifications in terms of Linear Temporal Logic  Extension to symbolic control design for CPS with multiple plants and computing units communicating via nonideal communication infrastructure Correct-by-design embedded control software 11/13
  98. Model: Networks of Finite State Machines Assumptions:  no continuous and/or hybrid dynamics  ideal communication infrastructure Done: [Pola et al.; Automatica-2016 ?]  Decentralized observers detecting instantaneously faults/criticalities in CPS  Model reduction via bisimulation theory To be done:  Extension to CPS with continuous and/or hybrid dynamics and with nonideal communication infrastructure  Extension to opacity [Mazare et., WITS 2004], i.e. to keep secret a set of states of an FSM with respect to all possible measurements on the system Detection of Faults and/or Criticalities in CPS 12/13
  99. Additional expertise required:  From Telecommunication Engineering to set up a comprehensive model of communication infrastructures  From Embedded Systems Engineering to set up a comprehensive model of hardware/software infrastructures  From Computer Science to design efficient algorithms for analysis and controllers’ synthesis The need for an interdisciplinary approach 13/13
  100. Autonomous Vehicle and MicroGrids as CPS Elena De Santis Introduction Traffic Control Motivations Autonomous Vehicle Power Systems Motivations DC Microgrid Conclusions Autonomous Vehicle and MicroGrids as CPS: Challenges and Opportunities Elena De Santis L’Aquila University Center of Excellence DEWS L’Aquila, Jenuary 26th 2016 1/17 Autonomous Vehicle and MicroGrids as CPS
  101. Autonomous Vehicle and MicroGrids as CPS Elena De Santis Introduction Traffic Control Motivations Autonomous Vehicle Power Systems Motivations DC Microgrid Conclusions Index 1 Introduction 2 Traffic Control Motivations Autonomous Vehicle 3 Power Systems Motivations DC Microgrid 4 Conclusions 2/172/17 Autonomous Vehicle and MicroGrids as CPS
  102. Autonomous Vehicle and MicroGrids as CPS Elena De Santis Introduction Traffic Control Motivations Autonomous Vehicle Power Systems Motivations DC Microgrid Conclusions Presentation outline Traffic Control: Development of an Adaptive Cruise Control model able to imitate human driver behaviour Power Systems Control: Analysis and control of a Direct Current microgrid connected to renewables, storage systems and loads 3/173/17 Autonomous Vehicle and MicroGrids as CPS
  103. Autonomous Vehicle and MicroGrids as CPS Elena De Santis Introduction Traffic Control Motivations Autonomous Vehicle Power Systems Motivations DC Microgrid Conclusions CPS 4/174/17 Autonomous Vehicle and MicroGrids as CPS
  104. Autonomous Vehicle and MicroGrids as CPS Elena De Santis Introduction Traffic Control Motivations Autonomous Vehicle Power Systems Motivations DC Microgrid Conclusions Traffic Control Microscopic approach: each element is analyzed (ex: mechanical laws) Macroscopic approach: the elements together are analyzed (ex: kinetic gas theory) Mesoscopic approach: macroscopic quantities are introduced in the microscopic approach! 5/175/17 Autonomous Vehicle and MicroGrids as CPS
  105. Autonomous Vehicle and MicroGrids as CPS Elena De Santis Introduction Traffic Control Motivations Autonomous Vehicle Power Systems Motivations DC Microgrid Conclusions Why Human-Inspired? BREAKING NEWS! 6/176/17 Autonomous Vehicle and MicroGrids as CPS
  106. Autonomous Vehicle and MicroGrids as CPS Elena De Santis Introduction Traffic Control Motivations Autonomous Vehicle Power Systems Motivations DC Microgrid Conclusions State of the art 7/177/17 Autonomous Vehicle and MicroGrids as CPS
  107. Autonomous Vehicle and MicroGrids as CPS Elena De Santis Introduction Traffic Control Motivations Autonomous Vehicle Power Systems Motivations DC Microgrid Conclusions Hybrid systems H = (Q, X, f , Init, Dom, E, G, R) Q = {q1, q2, ...} is the set of discrete states; X = Rn is the continuous state space; f = {fi , qi ∈ Q} is a vector field; Init ⊆ Q × X is the set of initial conditions; Dom(·) : Q → 2X ; E ⊆ Q × Q is the set of edges; G(·) : E → 2X is a map describing guard conditions; R(·, ·) : E × X → 2X is a reset. 8/178/17 Autonomous Vehicle and MicroGrids as CPS
  108. Autonomous Vehicle and MicroGrids as CPS Elena De Santis Introduction Traffic Control Motivations Autonomous Vehicle Power Systems Motivations DC Microgrid Conclusions Discrete States and related Domains Important Control based on information from LEADER + ENVIRONMENT 9/179/17 Autonomous Vehicle and MicroGrids as CPS
  109. Autonomous Vehicle and MicroGrids as CPS Elena De Santis Introduction Traffic Control Motivations Autonomous Vehicle Power Systems Motivations DC Microgrid Conclusions Power Systems Control 10/1710/17 Autonomous Vehicle and MicroGrids as CPS
  110. Autonomous Vehicle and MicroGrids as CPS Elena De Santis Introduction Traffic Control Motivations Autonomous Vehicle Power Systems Motivations DC Microgrid Conclusions Change of paradigm Energy Production Energy Transportation 11/1711/17 Autonomous Vehicle and MicroGrids as CPS
  111. Autonomous Vehicle and MicroGrids as CPS Elena De Santis Introduction Traffic Control Motivations Autonomous Vehicle Power Systems Motivations DC Microgrid Conclusions DC Microgrid Definition Microgrid concept: a cluster of loads and microsources operating as a single controllable system that provides power to its local area. 12/1712/17 Autonomous Vehicle and MicroGrids as CPS
  112. Autonomous Vehicle and MicroGrids as CPS Elena De Santis Introduction Traffic Control Motivations Autonomous Vehicle Power Systems Motivations DC Microgrid Conclusions Framework 13/1713/17 Autonomous Vehicle and MicroGrids as CPS
  113. Autonomous Vehicle and MicroGrids as CPS Elena De Santis Introduction Traffic Control Motivations Autonomous Vehicle Power Systems Motivations DC Microgrid Conclusions Compressed Sensing Problem: Find a sparse solution to the under-determined set of equations: 14/1714/17 Autonomous Vehicle and MicroGrids as CPS
  114. Autonomous Vehicle and MicroGrids as CPS Elena De Santis Introduction Traffic Control Motivations Autonomous Vehicle Power Systems Motivations DC Microgrid Conclusions Why is interesting? 15/1715/17 Autonomous Vehicle and MicroGrids as CPS
  115. Autonomous Vehicle and MicroGrids as CPS Elena De Santis Introduction Traffic Control Motivations Autonomous Vehicle Power Systems Motivations DC Microgrid Conclusions References Safe Human-Inspired Mesoscopic Hybrid Automaton for Longitudinal Vehicle Control, A. Iovine, F. Valentini, E. De Santis, M. Di Benedetto, M. Pratesi, 5th IFAC Conference on Analysis and Design of Hybrid Systems (ADHS’15), Atlanta, 14-16 October 2015 A Safe Human-Inspired Mesoscopic Hybrid Automaton for Autonomous Vehicles, A. Iovine, F. Valentini, E. De Santis, M. Di Benedetto, M. Pratesi, to be submitted to IFAC journal Nonlinear Analysis: Hybrid Systems (NAHS) Management of the Interconnection of Renewables and Storages into a DC Microgrid, A. Iovine, S. B. Siad, G. Damm, A. Benchaib, F. Lamnabhi-Lagarrigue, E. De Santis, M. D. Di Benedetto, draft Secure Estimation for Wireless Tracking Control under Denial-of-Service Attacks G.Fiore, Y.H. Chang, Q.Hu, C. Tomlin, M.D. Di Benedetto, draft 16/1716/17 Autonomous Vehicle and MicroGrids as CPS
  116. Autonomous Vehicle and MicroGrids as CPS Elena De Santis Introduction Traffic Control Motivations Autonomous Vehicle Power Systems Motivations DC Microgrid Conclusions Thanks for your attention! Any Questions? 17/1717/17 Autonomous Vehicle and MicroGrids as CPS
  117. Henry Muccini henry.muccini@univaq.it DISIM Dept. of Information Engineering, Computer Science and Mathematics University of L’Aquila, Italy DEWS Centre of Excellence on Design Methodologies of Embedded Controllers, Wireless Interconnect and Systems-on-chip - University of L’Aquila, Italy
  118. SEA Group Which architectural styles? Objective 1: Discovering best practices in Architecting Cyber-Physical Systems Objective 2: Discovering self-adaptation practices in Architecting Cyber-Physical Systems Collaborating CPS components • Which architectural style? • How to describe the architecture of a CPS? • Which are the critical architecture decisions? • How to assess the quality of such a model?
  119. SEA Group Goal: to analyze the state-of-the art in architecting (self-adaptive) CPS Method: Systematic Literature Review Output: a classification of the most frequent practices used for architecting CPS More info: http://dl.acm.org/citation.cfm?id=2797453 Contact info: henry.muccini@univaq.it
  120. SEA Group Models Interoperability Multi View Management (DS)Language Extensibility Usable & Analytic DSL Group Design Decision