Embracing Complexity. Gavin Walker Development Manager

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1 Embracing Complexity Gavin Walker Development Manager 1

2 MATLAB and Simulink Proven Ability to Make the Complex Simpler 1970 Stanford Ph.D. thesis, with thousands of lines of Fortran code 2

3 MATLAB and Simulink Proven Ability to Make the Complex Simpler Credit: SwRI 3

4 So why am I talking about EMBRACING complexity? 4

Possible Grand Challenges Zero automotive traffic fatalities, injuries minimized, and significantly reduced traffic congestion and delays Blackout-free electricity generation and distribution

5 Possible Grand Challenges Zero automotive traffic fatalities, injuries minimized, and significantly reduced traffic congestion and delays Blackout-free electricity generation and distribution Perpetual life assistants for busy, older or disabled people Energy-aware buildings Location-independent access to world-class medicine Raj Rajkumar, Carnegie Mellon University Kang Shin, University of Michigan Insup Lee, University of Pennsylvania Excerpted, with permission 5

6 How do we do it? Collaborate Model Simulate Automate 6

Functions in Today s Automobile Ride Control Traction Control Instrumentation Driver Drowsiness Navigation Steering Engine Infotainment Adaptive Front Lighting Systems Crash Avoidance Obstacle

7 Functions in Today s Automobile Ride Control Traction Control Instrumentation Driver Drowsiness Navigation Steering Engine Infotainment Adaptive Front Lighting Systems Crash Avoidance Obstacle Detection Power Management Lights Doors ABS Transmission Stability Controls Wireless Connectivity Windows Adaptive Cruise Control Airbags Passenger Detection Climate Controls Voice Recognition 7

8 Functions in Today s Automobile Automatic Cruise Control (ACC) 8

9 Functions in Today s Automobile Automatic Cruise Control (ACC) 9

10 Functions in Today s Automobile ACC integrates with engine control electronic stability program braking system 10

11 Platform for Collaboration Software Architecture Definition Behaviour Modelling & Code Generation BSW Configuration & RTE Generation 11

12 Neuroimaging of Brain Activity PET SPECT EEG MEG fmri Function? Structure? Connectivity? 12

13 Platform for Collaboration Courtesy: Wellcome Trust Centre for Neuroimaging, UCL, UK 13

14 Platform for Collaboration Courtesy: Wellcome Trust Centre for Neuroimaging, UCL, UK 14

15 Data Analysis Action Knowledge Information Data Physical Sensors 15

16 Data Analysis Application Action Understanding Knowledge Information Organization Data Observation Physical Sensors 16

17 Data Analysis Application Databases Data warehouses Understanding Files, programs, Web Organization DAQ and instrumentation Observation Sensing Collecting Data Acquisition Cameras 17

18 MPG Acceleration Displacement Weight Horsepower MPG Acceleration Displacement Weight Horsepow er Embracing Complexity Data Analysis Application Exploratory Analysis Understanding Visualization Organization Filtering Signal Analysis Data Reduction Plotting Observation Sensing Collecting Data Acquisition Data Processing 18

active power per-unit MSE Embracing Complexity Data Analysis Application Understanding Visualization Predictive Analytics Frequency/Time-domain Organization Filtering

Frequency Image, video Geospatial etc. Estimation and Prediction 1 0.9 0.8 0.7 0.6 0.

19 active power per-unit MSE Embracing Complexity Data Analysis Application Understanding Visualization Predictive Analytics Frequency/Time-domain Organization Filtering Signal Analysis Data Reduction Plotting Observation Sensing Collecting Data Acquisition Analytics Pre-built algorithms Evaluate, compare, customize Domains Time & Frequency Image, video Geospatial etc. Estimation and Prediction Regression time secs Classification NN measured 1.2 x Linear Non-linear Nonparametric Decision Tree Ensemble Method Neural Network Support Vector Machine turbine number

20 Data Analysis Application Reporting Apps Scalable Deployment Understanding Visualization Predictive Analytics Frequency/Time-domain Reports MATLAB Apps Organization Filtering Signal Analysis Data Reduction Plotting Integration into Existing Systems Excel Observation Sensing Collecting Data Acquisition Feedback for Design & Operations 20

21 Embracing Complexity From Data to Knowledge to Action 21

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24 Embracing Complexity Modelling and Simulation 24

25 Modelling and Simulation Simulink Master Class: Physical Modelling with SimScape 25

26 Collaborate Model Simulate Automate 26

27 Automation Automated task can be done by anyone 27

28 Automation Automation brings mistakeproofing 28

29 Automation Automate the common but especially the complex 29

30 Automatic Code Generation Rapid prototyping HIL testing Embedded systems The Festo Bionic Handling Assistant. Image Festo AG. PLCs FPGAs DSPs Microcontrollers 30

31 Automatic Code Generation Video Festo AG. 31

32 Automatic Code Generation Using Simulink for Model-Based Design enables us to develop the sophisticated pneumatic controls required for the Bionic Handling Assistant and other mechatronic designs. With Simulink PLC Coder, it is now much easier to get from a design to a product. Dr. Rüdiger Neumann, Festo 32

33 Automatic Code Generation Rapid prototyping HIL testing Embedded systems The Festo Bionic Handling Assistant. Image Festo AG. PLCs FPGAs DSPs Microcontrollers The Alenia Aermacchi M-346. Automatic Code Generation Certified Process 33

Alenia Aermacchi Develops Autopilot Software for DO-178B Level A Certification Challenge Develop the company s first DO-178B Level A certified autopilot system Solution Use Model-Based Design to

certification up to 30% shorter Time-to-flight reduced by 20% Low-level certification activities automated The Alenia Aermacchi M-346.

34 Alenia Aermacchi Develops Autopilot Software for DO-178B Level A Certification Challenge Develop the company s first DO-178B Level A certified autopilot system Solution Use Model-Based Design to model the system and software design, verify requirements coverage, generate code, and produce reports and other artifacts for the certification authority Results Requirements review for certification up to 30% shorter Time-to-flight reduced by 20% Low-level certification activities automated The Alenia Aermacchi M-346. For us, a key advantage of Model-Based Design is the ability to concentrate on design and development instead of lowlevel coding, verification, and certification tasks. The result is higher quality, DO-178B certified software, and faster iterations. Massimiliano Campagnoli Alenia Aermacchi 34

35 Building the Foundations Innovative Course Design Technical Computing: Enhancing Numerical Analysis Education with MATLAB 35

36 Building the Foundations LEGO MINDSTORMS NXT Student Contest 36

37 Building the Foundations Easy-to-Build Devices Programmable Hardware + Model-Based Design: + Modelling, Simulation, and Real-Time Testing for Model-Based Design 37

38 Building the Foundations LEGO MINDSTORMS NXT Student Contest Cambridge University ECO Racing 38

39 Possible Grand Challenges Zero automotive traffic fatalities, injuries minimized, and significantly reduced traffic congestion and delays Blackout-free electricity generation and distribution Perpetual life assistants for busy, older or disabled people Energy-aware buildings Location-independent access to world-class medicine 39

40 Embracing Complexity Used by permission of Prof. Chris Gerdes, Stanford University School of Engineering 40

41 Embracing Complexity Used by permission of Prof. Chris Gerdes, Stanford University School of Engineering 41

42 We want to use every bit of the car s capability to be as safe as possible. We want to develop autonomous vehicles that can avoid any accident that is physically possible to avoid. Professor Chris Gerdes Stanford University 42

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44 Embracing Complexity and instinctive driver reactions Used by permission of Prof. Chris Gerdes, Stanford University School of Engineering 44

45 Embracing Complexity and instinctive driver reactions Used by permission of Prof. Chris Gerdes, Stanford University School of Engineering 45

46 Embracing Complexity adding drift and countersteering Used by permission of Prof. Chris Gerdes, Stanford University School of Engineering 46

47 Embracing Complexity Autonomous Vehicles 47

48 Autonomous Traffic Management Can use information from other vehicles when available Robust when other vehicles aren t similarly equipped 48

49 Collaborate Model Simulate Automate 49

50 What will you need to embrace complexity 50

Getting to Smart Paul Barnard Design Automation

Getting to Smart Paul Barnard Design Automation paul.barnard@mathworks.com 2012 The MathWorks, Inc. Getting to Smart WHO WHAT HOW autonomous, responsive, multifunction, adaptive, transformable, and smart