This document provides an overview of a course on real time embedded control systems using model based design concepts. The course aims to show a design path for real time embedded systems starting with system level simulation and ending with real time implementation of control algorithms. It covers topics such as introduction to MATLAB and Simulink, physical system modeling, control systems design, embedded coding, and advanced topics like state machines. Model based design is emphasized, with a focus on graphical system modeling, executable specifications, and automatic code generation.

1. REAL TIME EMBEDDED CONTROL SYSTEMS
USING MBD CONCEPT
Eng. Mahmoud Hussein
18-oct-19 RTECS 2019
1

2. Contact Person
2
 Course Instructor
 Mahmoud Hussein
 Lecturer assistant, French university
 Email: Mahmoud.ahmed2013@gmail.com
 Mobile: 01007148378
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3. Rules
13
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4. ‫الذكية‬ ‫للصواريخ‬ ‫القومى‬ ‫المشروع‬
4
Which methodology will you use to develop a SAM?
A: Manually Write Assembly
B: Manually Write C-Code
C: MBD: Auto-Code Generation
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5. Welcome to
Real Time Embedded Control Systems
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6. Course Objective
6
 The course shows one design path for real time embedded systems.
 It starts by system level simulation based design.
 It ends by real time implementation of control algorithms.
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7. Course Syllabus
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 Part 1 : Introduction to Matlab and Simulink
 Recognize MATLAB and Simulink Environment
 MATLAB Fundamentals and Basic Operations
 M-File and Programming Basics
 Data Visualization (2D/3D)
 State flow.
 S-function
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8. Course Syllabus
8
Part 2 : Basics and Modelling
 Introduction to Real Time Embedded Control Systems
 Physical System Modelling of Mechanical Systems
 Dynamic SystemAnalysis
 Physical System Modelling of Electrical Systems
 Modelling Electric Motors
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9. Course Syllabus
9
Part 3: Control and PID
 Control Systems
 PID Controller
 Tuning PID Controllers
 Numerical Integration
 PID Advanced Topics
 Digital Controller Design
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10. Course Syllabus
10
Part 4: Advanced Topics
 Embedded Coder .
 State Machine Concept.
 MathWorks Automotive Advisory Board- MAAB(Optional)
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11. T
ools
11
 Matlab & Simulink
 Control System Toolbox
 Simulink Control Design
 State Flow
 Embedded Coder
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12. Domains of Appplication of Embedded
Systems
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Control Systems
• Vehicles
• Appliances
General Computing
• ATM
• Video Games
Signal Processing
• Radar, Sonar
• Video Processing
Communication &
Networking
• Moblie Phones
• Servers

13. Real Time Embedded Control Systems Applications
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Steam Turbine
Control
Flight Control
Print Head
Control
Navigation
Control
Engine Control
Dryer Cycle
Control
Medical Device
Control

14. Model Based Design
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15. Model Based Design
• In Model-Based Design, a system model is at the center of the
development process, from requirements development, through
design, implementation, and testing.
• The model is a graphical representations of math-based simulation
methods.
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16. Elements of Model Based Design
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17. Elements of Model Based Design
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Model
Executable
Specificatio
n
Design with
Simulation
Automatic
Code
Generation
Continuous
Test and
Verification

18. Executable Specification
• Executable
• The model can be simulated to illustrate functional behavior.
• Specification
• An explicit design intended to meet certain requirements.
18

19. Elements of Model Based Design
19
Model
Executable
Specification
Design with
Simulation
Automatic
Code
Generation
Continuous
Test and
Verification

20. Design with Simulation
• Use simulate during the design stage to verify that the model meets
the requirements, detect and correct errors early in the design stage
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21. Elements of Model Based Design
21
Model
Executable
Specificatio
n
Design with
Simulation
Automatic
Code
Generation
Continuous
Test and
Verification

22. Coding Traditional Approach
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Problems:
• Communication
• Ambiguity of specs
• Resource conflicts
 Large turnaround time!
Production
Code
Specs
Function Developer
 algorithm
knowledge
#include <math.h>
if (a > 0)
ki = 0.4*x+z1;
Software Specialist
 implementation
& coding knowledge
Time

23. Automatic Code Generation
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Function Developer
 algorithm
knowledge
Software Specialist
 implementation
knowledge
Coding knowledge:
• ANSI-C
• language extensions
• assembly language
• processor architecture
 … and how to
optimally use it!
Code Generator
 coding knowledge

24. Automatic Code Generation Features
• Scheduling and integration with RTOS
• MISRA C
• Autosar
• Customizable code
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25. Automatic Code Generators
• TargetLink from dSPACE, since 1999
• Real-Time Workshop Embedded Coder from
MathWorks
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26. Automatic Code Generation
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Implementation Model
(fixed-point)
C Code
Code generator
Compiler
(Linker)
Host PC
Target
Physical Model
(floating-point)
Cross-compiler
(Linker / Loader)

27. Automatic Code Generation
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Implementation Model
(fixed-point)
C Code
Code generator
Compiler
(Linker)
Host PC
Target
Physical Model
(floating-point)
Cross-compiler
(Linker / Loader)

28. Elements of Model Based Design
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Model
Executable
Specification
Design with
Simulation
Automatic
Code
Generation
Continuous
Test and
Verification

29. Continuous Test and Verification
• Deign test cases to test the model
• Reuse test cases to test software implementation
• Simulation models enable to test conditions that would be
destructive or cost-prohibitive to run in the lab or on the road
• Automatically generating test cases from the model to ensure
Modified Condition/Decision Coverage (MC/DC)
• Writing the MC/DC tests would take as least as long as the original design
effort
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30. RTECS 2010
30



test output
result
comparison
physical model
implementation model
test stimuli
C code (target)
ECU
MiL (physical model)
MiL (impl. model)
SiL
PiL
C code (host)

31. Model in the Loop (MIL)
• Proof of Concept and Design Optimization
• Pure simulation
• Modelling of both the application and the
environment may be wrong
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Controller
running in e.g. Simulink
Plant
running in e.g. Simulink

32. Software in the Loop (SIL)
• Compiled application
• Simulation at an early stage
• Accelerated simulation
• Low cost & short development time
• Non real-time
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Controller
running as binary
Plant
running in e.g. Simulink

33. Processor in the Loop (PIL)
• Determining Memory Usage
• Determining the time needed for the Algorithm
• Assures that the tool chain does not cause problems
• Determining problems related to the resolution of
the processor (16 bits, 32 bits, fixed point)
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Controller
running on target HW
Plant
running in e.g. Simulink

34. MIL SIL PIL
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35. Hardware in the Loop (HIL)
• HIL system replaces the real world environment
• Production hardware can be tested without any
changes
• Application is executed in real time on an
appropriate hardware platform
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Controller
running on target
Plant
Running in real-time on a HIL
Simulator

36. HIL Simulator: dSPACE
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Dynamic
Models
Processor Board
I/O Board
GUI and
Automation
Diagn. Tool
ECU
Loads
FIU

37. User Interface for Interactive Use of the HIL System
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38. Networked HIL Simulation
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Vehicle
dynamics
Comfort
(central unit)
Transmission Engine
GigaLink
CAN
2004 Audi A6

39. Networked HIL Simulation
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Mercedes-Benz 2005 A-Class
Breadboard Table
appr. 8 powertrain
and chassis ECUs
appr. 20 body ECUs

40. What is a Model?
RTECS 2014
15
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41. What is a Model?
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A model is a representation of a real system focusing onsome
important aspects
 Scale model
 Represents geometric proportions
 Flight simulator
 Focuses on flight dynamics
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42. What is a Model?
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 In Model-Based Design, a system model is at the center of the
development process, from requirements development, through
design, implementation, and testing.
 What is meant here by model is a graphical representations of
the system
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43. Model Based Design Rationale
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 Think: can you draw a rectangle with 3 lines
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44. Model Based Design Rationale
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 Executable specification
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45. Model Based Design Rationale
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 Automatic Code
Generation
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46. Model Based Design Rationale
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1. Graphical Representation
2. Executable Specification
3. Automatic Code Generation
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47. Physical Systems
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48. Definition of System
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 From engineering point of view, a system is defined as an
interconnection of many components that act together to
perform a certain objective
 Automobile
 Machine tool
 Robot
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49. Physical Systems Classification
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Physical
System
Static System Dynamic
System
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50. Static System
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 Output of the system depends only on the current input
 The system has no memory
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51. Dynamic System
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 Output of the system depends on the current input as well as
previous inputs/outputs
 The system has internal memory
A dynamic system can be represented mathematically
using differential equations
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52. What is a Real Time System?
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53. Real-Time System
43
 A real-time system is a software system where the correct
functioning of the system depends not only on the results
produced by the system but also on the time at which these
results are produced.
 The system has "real-time constraints"
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54. Hard Real-Time System
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 A hard real-time system is a system whose operation is incorrectif
results are not produced according to the timing specification.
 Car engine control system is a hard real-time system
 because a delayed signal may cause engine failure or damage
 Flight Control System
 Airbag crash detection system
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55. Hard Real-Time System
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 Delay = Failure
 Time granularity
 Millisecond
 RequiredAnalysis
 Worst possible scenario
 Need for redundancy
 To meet safety requirements
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56. Hard Real-Time System Example
46
 Airbag crash detection system
 Airbag must inflate between 10 and 20 msec from the detection of a crash
 Not too early—since this would make the airbag deflate before it can catch
the passenger
 Nor too late—since the airbag could then injure the passenger by blowing up
in his face and/or catch him too late to prevent his head from banging into the
steering wheel
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57. Soft Real-Time System
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 A soft real-time system is a system whose operation is
degraded if results are not produced according to the specified
timing requirements.
 Non-safety-critical system
 Keypad input
 Message visualization
 System status representation
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