1. ID 023C: Model-Based Control Design
Design with a Systematic Perspective
SimuQuest Inc.
Raymond Turin
Co-Founder
12 October 2010
Version: 1.1

2. Dr. Raymond Turin Co-Founder, Chief Technical Officer
Responsible for managing control system and plant model development
Lead Developer of Enginuity, an engine modeling tool package that supports virtual control system development.
PREVIOUS EXPERIENCE:
Many years of industrial experience in model-based engine control design and plant modeling
Staff Research Engineer, General Motors Research and Development
Ph. D. in Mech. Eng. from ETH Zurich, Switzerland

3. Renesas Technology and Solution Portfolio
Microcontrollers & Microprocessors #1 Market share worldwide *
Solutions for Innovation
Analog and Power Devices #1 Market share in low-voltage MOSFET**
ASIC, ASSP & Memory Advanced and proven technologies
In the session 110C, Renesas Next Generation Microcontroller and Microprocessor Technology Roadmap, Ritesh Tyagi introduces this high level image of where the Renesas Products fit. The big picture.
* MCU: 31% revenue basis from Gartner "Semiconductor Applications Worldwide Annual Market Share: Database" 25 March 2010
** Power MOSFET: 17.1% on unit basis from Marketing Eye 2009 (17.1% on unit basis).

4. Renesas Technology and Solution Portfolio
Microcontrollers & Microprocessors #1 Market share worldwide *
Solutions for Innovation
ASIC, ASSP & Memory Advanced and proven technologies
Analog and Power Devices #1 Market share in low-voltage MOSFET**
This is where our session, 023C: Auto Code Generation: The Shortest Distance From Idea to Implementation, is focused within the ‘Big picture of Renesas Products’
* MCU: 31% revenue basis from Gartner "Semiconductor Applications Worldwide Annual Market Share: Database" 25 March 2010
** Power MOSFET: 17.1% on unit basis from Marketing Eye 2009 (17.1% on unit basis). 4

5. Microcontroller and Microprocessor Line-up
Up to 1200 DMIPS, 45, 65 & 90nm process
Video and audio processing on Linux
Server, Industrial & Automotive
Superscalar, MMU, Multimedia
Up to 500 DMIPS, 150 & 90nm process
600uA/MHz, 1.5 uA standby
Medical, Automotive & Industrial
High Performance CPU, Low Power
Up to 165 DMIPS, 90nm process
500uA/MHz, 2.5 uA standby
Ethernet, CAN, USB, Motor Control, TFT Display
High Performance CPU, FPU, DSC
Legacy Cores
Next-generation migration to RX
H8S
H8SX
M16C
R32C
Here are the MCU and MPU Product Lines, I am not going to cover any specific information on these families, but rather I want to show you where this session is focused.
General Purpose
Ultra Low Power
Embedded Security
Up to 10 DMIPS, 130nm process
350 uA/MHz, 1uA standby
Capacitive touch
Up to 25 DMIPS, 150nm process
190 uA/MHz, 0.3uA standby
Application-specific integration
Up to 25 DMIPS, 180, 90nm process
1mA/MHz, 100uA standby
Crypto engine, Hardware security 5

6. Microcontroller and Microprocessor Line-up
Up to 1200 DMIPS, 45, 65 & 90nm process
Video and audio processing on Linux
Server, Industrial & Automotive
Superscalar, MMU, Multimedia
All Of Them!
Up to 500 DMIPS, 150 & 90nm process
600uA/MHz, 1.5 uA standby
Medical, Automotive & Industrial
High Performance CPU, Low Power
Up to 165 DMIPS, 90nm process
500uA/MHz, 2.5 uA standby
Ethernet, CAN, USB, Motor Control, TFT Display
High Performance CPU, FPU, DSC
Legacy Cores
Next-generation migration to RX
H8S
H8SX
M16C
R32C
These are the products where this presentation applies
General Purpose
Ultra Low Power
Embedded Security
Up to 10 DMIPS, 130nm process
350 uA/MHz, 1uA standby
Capacitive touch
Up to 25 DMIPS, 150nm process
190 uA/MHz, 0.3uA standby
Application-specific integration
Up to 25 DMIPS, 180, 90nm process
1mA/MHz, 100uA standby
Crypto engine, Hardware security 6

7. Innovation: Innovate Fast and Focused
code generation
system testing
driver integration
system integration
Use Virtual Development Practice and
Focus on Feature Design and
IP Development

8. Agenda Plant Modeling Controller Development Controller Validation
HIL Testing
Q&A

9. Key Takeaways By the end of this session you will:
Understand the idea of model-based development
Understand the importance of plant models for virtual design and validation
Understand the power of interactive model-based design and validation
Know a lot more about engines and engine control

10. Terminology
Plant: A dynamic system including sensors and actuators that is being controlled using a microcontroller
Controller: An algorithm that is implemented on a microcontroller and forces the plant to behave in a predefined way
HIL: Hardware-In-the-Loop
VPM: Virtual Processor Model

11. The Big Picture

12. Why Model-Based Reuse recurring and tested elementary features
Summer, adder, divider, multiplier, integrator, etc.
Embed features within graphical modeling environment
Use feature model blocks
Design and link complex features graphically
Avoid fat-fingering
Test feature design as you go
Perform functional feature tests without writing any code
Validate integrated system before HW is available
Rapid deployment of fully integrated system
Embed driver code in the form of model blocks
Use automated code generation
Use HIL and VPM for system testing

13. Comparison Traditional development Model-Based Development
requirements
system testing
(HIL testing
VPM testing)
feature integration
system integration
feature design
requirements
feature design
system testing
driver integration
HIL testing
VPM testing
system integration
feature testing
code generation
feature integration

14. Key Concepts Use model as single repository for all process artifacts
Separate target dependent/target independent functionality
Implement centralized data management
Implement “wireless” signal transfer

15. Plant Modeling

16. Idea Synthesize control feature based on plant characteristics
Test and validate feature design in virtual setting
Use model in lieu of target HW
Plant
Model
Control
Feature
plant output
feature output
feature input
Signal
Conditioning

17. Plant Model Overview Control Design Model Simulation Model
Support for model-based controller synthesis
Simple model described by ordinary (linear) DE
Appears in mathematical description of controller equation
Example, internal model design, LQ design, Hinf-design, Kalman-Filter
Simulation Model
Model to support HW design or to validate control design
Distributed parameters, partial DE
support HW design (engine, machinery, etc.)
Examples:
computational flow dynamics models
finite element models
Zero-dimensional, ordinary DE
Control system validation and hardware in the loop (HIL) validation
physics-based, first principles, physical laws
mean-Value, first principles, look-up
Real-time capable

18. Controls Oriented Simulation Model
Capture relevant effects between actuators and sensors
Computational Efficiency
Interactive control system design
Real-time execution (HIL)
actuators
sensors

19. Plant Model Characterization
Physics-based
Based on first principles and physical laws (kinematic, dynamic, thermodynamic, and geometric relationships)
Mass, momentum and energy storage (Newton, Lagrange, etc.)
Gas laws
Fick’s laws …
Regression-based
Based on empirical data
Step-response/Frequency response
Non-linear approximation
Neural network
Look-up (mean-value)

20. Modeling Example: Physics-Based Modeling
Mathematical Description
Flow in
Flow out
Mass Balance
Tank level
Water tank model
Flow in
Flow out
Tank level

21. Modeling Example: Physics-Based Modeling
Matlab Example
Tank diameter: 0.5 m
Orifice in diameter: 0.03 m
Orifice out diameter: 0.03 m

22. Modeling Example: Regression Based Modeling
Step-response
Frequency Response
Plant
Model
Plant
Model

23. Plant Model Examples Engine Control Power Window Control
Cylinder-by-cylinder engine model
Mean-value engine model
 Matlab Examples
Power Window Control
Combined motor and window model
 Matlab Example

24. Controller Design and Implementation

25. Control Fundamentals Open-Loop Control Closed-Loop Control
Inverse plant dynamic
Look-up (inverse mapping)
Example in Matlab
Closed-Loop Control
Signal feedback
Classical control
Fixed controller structure (P,PI,PID)
Modern (model-based) control
Intrinsic plant model
Examples: Linear Quadratic/Hinf/Internal Model Control
Stability and Robustness (e.g., Nyquist)
Plant limitations (e.g., system delays)
Un-modeled dynamics
Example in Matlab: Linear Quadratic Control
Plant (nominal and actual):
Objective:
Controller:

26. Case Study Development : Engine Control
Basic Control Inputs
Spark
Open-loop: lookup tables
Closed-loop: P-control
Fuel
Open-loop: calibration values
Closed-loop: switch-type
Throttle/Idle Bypass Control (Air)
Open-loop: look-up (driver input)
Closed-loop: PI torque control
Basic Control Implementation
Time-based control
Event-based control (crank-angle based)
spark
fuel
air

27. Engine Control: Open-Loop Control
Air Control
Open air valves to satisfy torque demand
Fuel metering control
Maintain stoichiometric A/F-ratio (emissions)
Air-charge estimation
Conversion into fuel pulse
Correction during special operating conditions
Spark control
Maximize torque (Base spark at MBT)
Conditional retard/advance (e.g. idle speed, knock control, etc.)

28. Example: Spark Control
Ignition delay
Relationship (Auto-Ignition):
Angular delay depends on:
Pressure (manifold pressure)
Engine speed
Maximum Brake Torque:
Spark [deg after TDC]
Torque [Nm]
TDC
MBT

29. Example: Spark Control Implementation
Conditions:
Start: Retard
Calibratable offset
(Constant/Lookup)
Idle Speed: Retard
Base Spark: Lookup
Empirical data (MBT)
y = f(nrpm,pm)
Spark [deg after TDC]
Torque [Nm]
TDC
Control Authority

30. Example: Air Charge Control
Feed-forward Torque Request
Driver request  Throttle (no control implementation)
Idle speed request  Idle Bypass Valve
Idle Bypass Valve Control:
Static relationship between air valve and engine brake torque
Conditional Adjustments
Start:
Calibratable offset
Idle speed
Calibratable base offset
AC adjustment
PRNDL adjustment
Normal
Calibratable lookup

31. Example: Fuel Control Base Fuel Conditional Adjustments
Assess air in cylinder (air charge estimation)
Meter fuel to achieve stoichiometric A/F-ratio
Conditional Adjustments
Start/Warm-up (enrichment)
Fill all fuel puddles
Account for lost fuel (fuel absorbed in oil)
Normal
Account for fuel delivery system and injector characteristics

32. Example: Fuel Control: Base Fuel
Air Charge Estimation: Intake Manifold Dynamics

33. Example: Fuel Control: Conditional Enrichment
Fuel Factor Calculation
Crank
Engine cranking, not firing
Fill puddles
Enrichment (lookup)
Start
Engine firing, key on
maximum torque
Normal
Stoichiometry
Lookup

34. Engine Control: Closed-Loop Control
Idle-speed control
air control (idle bypass valve)
spark control
Closed-loop fuel control
Stoichiometric A/F-ratio control
Implementation Issues
Time-based
all closed-loop control features
Event-based
open-loop fuel
open-loop spark

35. Closed-Loop Engine Control: Idle Speed
Measure engine speed
Increase air/spark-advance
 more torque
Decrease air/spark-advance
 less torque
Air control (Idle bypass valve)
Nominal valve opening calibratable open-loop control parameter
integral feedback control
spark control
Nominal spark retarded from MBT
proportional control

36. Closed-Loop Engine Control: Fuel (A/F-Ratio)
Concept: Measure O2 content in exhaust gas
Controller
Injector
O2-Sensor

37. Closed-Loop Engine Control: Fuel (A/F-Ratio)
Considerations
Sensor Characteristic
“Binary” information
Switch type control
Rich (reduce fuel)
Lean (increase fuel)
Transition (step)
0.6
0.7
0.8
0.9 1
1.1
1.2
1.3
0.0
0.1
0.2
0.3
0.4
0.5
1.0
1.4
rich
lean l
Us [V]
Us [V]
Uupper [V]
Ulower [V]
fl [-] 1
t [s]

38. Matlab Example
Engine Control Development

39. Controller Validation

40. Case Study Validation: Power Window Control
Overview
Control power window motion
Handle special cases
Detect obstacles

41. Control Validation: Requirements-based testing
Button up: window moves up
Button down: window moves down
Both buttons: stop moving
Obstacle: Stop moving
Test Design
Test #
Scenario
Control Inputs
Control Outputs
Requirement 1
Button up,
No obstacle
DsideDrvSwUp (= 1)
DsideDrvSwDn (= 0)
DriverPositionVolts
DrvMtrCurrentVolts
DrvMoveUp
DrvMoveDn
Move up;
Stop at limit; 2 …

42. Power Window Control Validation: Test Harness
Design test vectors
Design test vectors based on Requirements
Use Simulink Signal Builder to implement and store tests
Establish base line control model
Create functional prototype model with no regard for data types and style guide
Run tests until satisfactory functional behavior
Validate control system
Implement changes
Use stored test results from base-line model to validate controls

43. Matlab Example
Power Window Control Validation

44. Code Generation: Refer to Courses 020L,024L,024C

45. Questions?

46. Thank You!