Cyber Physical System

Review & PResentation

Cyber–Physical Systems As seen by

Abu Chowdhury John University of Louisiana at Lafayette Spring 2015

Presented for

Dr. Magdy A. Bayoumi & VLSI Group

edX & Barkley

outline CPS

outline Background ~ Emergence of Cyber Physical System ~ IOT vs CPS ~ Overview of edX Berkeley NI partnership A look inside Berkeley CPS ~ Intro to CPS, characteristic ~ Focus of CPS, Model Techniques ~ Design & Analysis ~ Practical Aspect Concluding Remark & Reference

The term & its predecessor

The term & its predecessor

The term & its predecessor

The term & its predecessor

The term & its predecessor

The term & its predecessor

Revolution : Industrial

Revolution : Power

Revolution : Digital

Revolution : Information

Local to Global

Local to Global

IOT vs CPS

IOT vs CPS

IOT vs CPS

IOT vs CPS

For all practical purposes – Today: more and less synonym

edX , Barkley & NI partnership

edX , Barkley & NI partnership

EDWARD LEE

SANJIT SESHIA

JEFF JENSEN

Introduces students to the design and analysis of cyber-physical systems --- computational systems that are integrated with physical processes UC Berkeley and NI have been collaborating for several years They have provided NI with tremendous product feedback Diffuse the technology to a much wider audience with edX's MOOC They expect subject to change quite dramatically over next few years

edX , Barkley & NI partnership Required Software NI LabVIEW NI LabVIEW myRIO Module NI LabVIEW MathScript Module NI LabVIEW Real-Time Module NI LabVIEW Statechart Module NI LabVIEW Robotics Module

Required Hardware NI myRIO -1950

Topics Covered

Download Contains Full Lab Exercise Manual PDF LabVIEW Project Files Eclipse Project Files

Preface Equipment Sensor Interfacing and Calibration Embedded Development Lab Setup Programming Embedded Systems Design of Cyber-Physical Systems Projects

outline Background ~ Emergence of Cyber Physical ~ IOT vs CPS ~ Overview of edX Berkeley NI partnership A look inside Berkeley CPS ~ Intro to CPS, characteristic ~ Focus of CPS, Model Techniques ~ Design & Analysis ~ Practical Aspect Concluding Remark & Reference

Intro to CPS Course Dr. Edwards started with meaning, history and examples It is Computational systems integrated with physical process

Intro to CPS Course Dr. Edwards started with meaning, history and examples It is Computational systems integrated with physical process He then put a challenge for the audience with a video clip that we would not want to allow in future. Can we make system to avoid this to happen?

Dr. Edwards believe in our lifetime we will see this

Challenge: Prevent this

Converge towards Active Safety

Converge towards Active Safety

Freightliner Unveils First Autonomous Semi-Truck Licensed to Drive Itself on Highways

Some Characteristics of CPS Reactive ~ defined by their interaction with the environment ~ must occurs at speed of the environment Concurrent ~ System + environment ~ Systems are concurrent even when the computational part of the system may not concurrent Heterogeneous ~ the hardware, the software components of the system ~ the physical processes which may involve various kinds of electrical and mechanical subsystems Networked ~ Networking is not something that a cyber-physical system needs to have. But today's cyber-physical systems are increasingly being networked ~ distributed, exposed to attacks

Focus of CPS course Modeling is the process of gaining a deeper understanding of a system through imitation.

Models express what a system does or should do.

Modeling Design Analysis

A model imitates a physical realization of a system.

Focus of CPS course Modeling is the process of gaining a deeper understanding of a system through imitation.

Models express what a system does or should do.

Design is the structured creation of artifacts. It specifies how a system does what it does.

Modeling Design Analysis

It answer the how it is done in the system

Focus of CPS course Modeling is the process of gaining a deeper understanding of a system through imitation.

Models express what a system does or should do.

Design is the structured creation of artifacts. It specifies how a system does what it does.

Analysis is the process of gaining a deeper understanding of a system through dissection.

It specifies why a system does what it does (or fails to do what a model says it should do).

Modeling Design Analysis

And often in analysis, you use computational tools to help you analyze complex systems

Focus of CPS course Modeling is the process of gaining a deeper understanding of a system through imitation.

Models express what a system does or should do.

Design is the structured creation of artifacts. It specifies how a system does what it does.

Analysis is the process of gaining a deeper understanding of a system through dissection.

It specifies why a system does what it does (or fails to do what a model says it should do).

Modeling Design Analysis

Its an iterative process of going through modeling, design, and analysis, and possibly iterating between them multiple times before we create the final implementation

Model vs Reality The Kopetz Principle Many (predictive) properties that we assert about systems (determinism, timeliness, reliability, safety) are in fact not properties of an implemented system, but rather properties of a model of the system.

We can make definitive statements about models, from which we can infer properties of system realizations. The validity of this inference depends on model fidelity, which is always approximate.

You will never strike Oil by drilling through the Map

But this does not, in any way, diminish the value of a map nevertheless is in its ability to make predictions and to give confidents in what the physical system will do when it's actually operating in the physical world

Model vs Reality The Kopetz Principle Many (predictive) properties that we assert about systems (determinism, timeliness, reliability, safety) are in fact not properties of an implemented system, but rather properties of a model of the system.

We can make definitive statements about models, from which we can infer properties of system realizations. The validity of this inference depends on model fidelity, which is always approximate.

The physical realization is not in fact deterministic as the model is Unpredictable ways if it's being crushed or if it's melting

Model vs Reality The Kopetz Principle Many (predictive) properties that we assert about systems (determinism, timeliness, reliability, safety) are in fact not properties of an implemented system, but rather properties of a model of the system.

We can make definitive statements about models, from which we can infer properties of system realizations. The validity of this inference depends on model fidelity, which is always approximate.

The physical realization again is the same. It's still a bundle of silicon and wires and the physical realization will match the model with high confidence but it's never perfect.

Model vs Reality The Kopetz Principle Many (predictive) properties that we assert about systems (determinism, timeliness, reliability, safety) are in fact not properties of an implemented system, but rather properties of a model of the system.

Differential equations are used to We can make definitive statements about describe physical dynamics as models, from which we can infer opposed to cyber behavior properties of system realizations. The validity of this inference depends on These models are structured very model fidelity, which is always differently, they have fundamentally different approximate. meanings and yet in CPS, we want to be able to bring these models together

Model vs Reality

A single-threaded imperative program and combine it with a differential equation model for a physical system that is perhaps being controlled by that imperative program, because of the differences in the modeling formalism, the semantics, the meanings of these models the combined behavior of these two models is in fact non- deterministic even when the individual behavior of the model is deterministic

Other Challenges in CPS CPS are Heterogeneous and Complex Design team are multi disciplinary

Other Challenges in CPS CPS are Heterogeneous and Complex Design team are multi disciplinary

Model based design Developing insight about a system, process, or artifact through imitation.

Create a mathematical model of all the parts of the embedded system

A model is the artifact that imitates the system, process, or artifact of interest.

Physical world Control system Software environment Hardware platform Network Sensors and actuators

A mathematical model is model in the form of a set of definitions and mathematical formulas/objects.

Once we're satisfied with our mathematical model, then we can construct implementation from that model.

When we create a mathematical model And this is often done manually today. of all of these, then we can analyze the Some parts of this are automated through behavior of this model model-based design tools, but not all.

Modeling Techniques mentioned Modeling Continuous Dynamic

Modeling Continuous Dynamic

Differential Equation – Physical process Actor Models Time-domain modeling Feedback Control

Finite State Machines – For Modal Behavior as in a controller, software Determinism, Receptiveness Trace – modeling an I/O behavior of an FSM Composition and Hierarchy – Synchronous/ Asynchronous composition, State chart

Hybrid Dynamic Timed and Hybrid Automata - Modal behavior & continuous dynamics Jumps and Flow

Design : Memory Architectures Types of Memory Volatile vs. non-volatile, SRAM vs. DRAM Memory maps Harvard architecture Memory-mapped I/O Memory Organization statically allocated stacks heaps (allocation, fragmentation, garbage collection) The Memory model of C Memory hierarchies scratchpads, caches, virtual memory) Memory protection segmented spaces

Design: Sensors & Actuators How Sensors Works, Interfacing & Basics

Sensors

Cameras Accelerometers Rate gyros Strain gauges Microphones Magnetometers Radar/Lidar Chemical sensors Pressure sensors Switches

Actuators

Motor controllers Solenoids LEDs, lasers LCD and plasma displays Loudspeakers Switches Valves

Design Issues with Sensors Calibration Relating measurements to the physical phenomenon vs. Can dramatically increase manufacturing costs Nonlinearity Measurements may not be proportional to physical phenomenon Correction may be required Feedback can be used to keep operating point in the linear region Sampling Aliasing Missed Events Noise Analog signal conditioning Digital filtering Introduces latency

Design : Concurrent programming With Interrupts I/O Mechanism in Software : Polling vs Interrupts Setting up Interrupts

Reasoning about Interrupt Driven Programs

Modeling & Analysis Specification & Temporal Logic The need for formal specification

Linear Temporal Logic

SpaceWire protocol

Practical Aspect

Practical Aspect

Practical Aspect

Practical Aspect

Practical Aspect

Practical Aspect

Concluding Remark Where we’ve been

Emphasis in Future course

Concluding Remark Where we’ve been Where we’ve been

Where we’ll go

Two world’s are coming together

Concluding Remark

Concluding Remark

Concluding Remark

Concluding Remark

Concluding Remark

Concluding Remark

Concluding Remark

Where we’ll go

Leading to interdisciplinary science and education

BRIGHT

REFERENCE References [1] https://courses.edx.org/courses/BerkeleyX/EECS49.1x [2] http://chess.eecs.berkeley.edu/eecs49/lectures/index.html [3] http://www.ideen2020.de/en/2993/whats-the-difference-between-cyber-physical-systems-and-the-internet-of-things/ [4] http://www.researchgate.net/post/What_is_the_difference_between_internet_of_things_and_cyber_physical_systems [5] http://www.tor.com/blogs/2011/06/norvigvschomskyandthefightforthefutureofai [6] http://scm.ulster.ac.uk/~scmresearch/SERG/ucamiiwaal2014/workshop-iot.html [7] “The cognitive revolution: a historical perspective”, George A. Miller, Princeton 2003 [8] https://sites.google.com/site/cpsbookelsevier/