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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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.
RTECS
7. Course Syllabus
7
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
RTECS
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
RTECS
11. T
ools
11
Matlab & Simulink
Control System Toolbox
Simulink Control Design
State Flow
Embedded Coder
RTECS
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
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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17. Elements of Model Based Design
17
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
20
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
RTECS 2010 23
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
24
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
RTECS 2019 27
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
RTECS 2019 28
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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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
31
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)
33
Controller
running on target HW
Plant
running in e.g. Simulink
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
41. What is a Model?
16
A model is a representation of a real system focusing onsome
important aspects
Scale model
Represents geometric proportions
Flight simulator
Focuses on flight dynamics
RTECS
42. What is a Model?
17
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
18
Think: can you draw a rectangle with 3 lines
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48. Definition of System
23
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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50. Static System
25
Output of the system depends only on the current input
The system has no memory
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51. Dynamic System
26
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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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
44
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
45
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
RTECS
57. Soft Real-Time System
47
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
RTECS
