The fundamental Process of control system design

发布于:2021-06-11 02:57:05

The fundamental Process of Control System Design

敖国强 高级软件工程师 Guoqiang.ao@mathworks.cn

? 2012 The MathWorks, Inc. 1

Agenda
?

System Simulation and Optimization
– Control algorithm design – Plant model design – System Simulation and Optimization

?

Control Algorithm Design
– Model architecture

?

Control Algorithm Test
– Unit test,Coverage test – MIL, SIL, PIL, HIL, RP

?

Deploy code to production
– Generating production code – System Integration – Calibration and field test 22

HEV system simulation and optimization

Sensors Actuators

s1 s3

s2

System

Controller

Plant

Controller

Plant

Controller

Plant

3

HEV system simulation and optimization

4

Agenda
?

System Simulation and Optimization
– Control algorithm design – Plant model design – System Simulation and Optimization

55

Simulink
– – – – Multidomain Graphical Interactive Hierarchical

Stateflow
– State machines – Flow charts – Natural language

6

Agenda
?

System Simulation and Optimization
– Control algorithm design – Plant model design – System Simulation and Optimization

77

Two Approaches for Physical Modeling from Mathworks

First-Principles
SimDriveline SimHydraulics Simulink Simscape SimMechanics SimElectronics SimPowerSystems

Data-Driven
Model Based Calibration
Neural

Simulink Design Optimization

Network
Toolbox

System Identification Toolbox

Symbolic Math

8

MBC
SimPowerSystems SimMechanics SimDriveline SimHydraulics SimElectronics

Simulink
Simscape

System simulation and optimization

MATLAB
9

Agenda
?

System Simulation and Optimization
– Control algorithm design – Plant model design – System Simulation and Optimization

10 10

System simulation and optimization

Max Optimization area Min Min Max

11

Agenda
?

System Simulation and Optimization
– Control algorithm design – Plant model design – System Simulation and Optimization

?

Control Algorithm Design
– Model architecture

12 12

Model architecture
?

Schedule
– Controlling the execution order and rate of components

?

Signal
– The creation of variable – The scope / ownership of variable – Specification of data type
Schedule

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Data/Parameter
– The creation of parameter – The scope / ownership of parameter – Specification of data type

Signal

Function

Data

?

Function
– Function prototype – Scope to functions – Specification of rate information
13

Function and Signal flow
Virtual Subsystem Atomic subsystem

Libraries

Model Reference

14

Agenda
?

System Simulation and Optimization
– Control algorithm design – Plant model design – System Simulation and Optimization

?

Control Algorithm Design
– Model architecture

?

Control Algorithm Test
– Unit test,Coverage test – MIL, SIL, PIL, HIL, RP

15 15

How Do You Test Your Models Today?

16

Increasing Confidence In Your Designs

Confidence
Traceability

Modeling and Coding Standards Checking

Model and Code Testing

Proving Design Properties and Code Correctness

Verification Method
17

Agenda
?

System Simulation and Optimization
– Control algorithm design – Plant model design – System Simulation and Optimization

?

Control Algorithm Design
– Model architecture

?

Control Algorithm Test
– – – – Unit test,Coverage test Modeling Standards MIL, SIL, PIL, HIL, RP Calibration and field test

18 18

Unit Test

19

Coverage test

20

Agenda
?

System Simulation and Optimization
– Control algorithm design – Plant model design – System Simulation and Optimization

?

Control Algorithm Design
– Model architecture

?

Control Algorithm Test
– Unit test,Coverage test – Modeling standards test – MIL, SIL, PIL, HIL, RP

21 21

Modeling Standards

? ?

?
? ? ?

MAB Style Guidelines DO-178B IEC-61508 ISO26262 MISRA-C:2004 Internal standards

?

Extensibility API

22

Agenda
?

System Simulation and Optimization
– Control algorithm design – Plant model design – System Simulation and Optimization

?

Control Algorithm Design
– Model architecture

?

Control Algorithm Test
– Unit test,Coverage test – Modeling standards test – MIL, SIL, PIL, HIL, RP

23 23

MIL

24

SIL

Code Generation

S-Function

25

PIL

Code Generation

26

Real-Time Testing Scenarios: Hardware-in-the-Loop (HIL) Simulation

Code Generation
Execution ? Host/Target/Target ? Real-time

Code Generation

Wiring and Signal Conditioning

ECU or MicroController

Real-Time Target Computer

27

Real-Time Testing Scenarios: Functional Rapid Prototyping

Code Generation

Execution ? Host/Target ? Real-time

Wiring and Signal Conditioning
Production Plant Hardware 28

Real-Time Target Computer

Agenda
?

System Simulation and Optimization
– Control algorithm design – Plant model design – System Simulation and Optimization

?

Control Algorithm Design
– Model architecture

?

Control Algorithm Test
– Unit test,Coverage test – MIL, SIL, PIL, HIL, RP

?

Deploy code to production
– Generating production code – System Integration – Calibration and field test
29 29

Algorithm code generation

30

Algorithm code generation

31

SAIC Motor Develops Embedded Control System for the Roewe 750 Hybrid Sedan Using Model-Based Design
Challenge
Develop the hybrid control unit for the Roewe 750 hybrid sedan
The Roewe 750 hybrid sedan.

Solution
Use MATLAB, Simulink, and Embedded Coder to model, simulate, verify, and generate production code for the embedded controller
“Three years ago, SAIC Motor did not have rich experience developing embedded control software. We chose Model-Based Design because it is a proven and efficient development method. This approach enabled our team of engineers to develop the highly complex HCU control logic and complete the project ahead of schedule.”
Jun Zhu SAIC Motor 32

Results
? 98% of production code generated ? Development from concept to production completed in 18 months ? Complete verification process established

Link to user story

Development of the Volt

Credit: General Motors LLC 2011

“We have a single source for how a particular function should behave. Automatic code generation using The MathWorks’ Real-Time Workshop Embedded Coder was vital to meeting Volt’s aggressive program timing.” Greg Hubbard Senior Manager

Nearly 100% of the software for many of Volt’s modules was generated automatically.

33

Eaton Reduces Emissions by 90% for Leading Freight Carrier's Hybrid Test Delivery Vehicles

Challenge
Develop a hybrid electric powertrain in less than one year that significantly reduces emissions and fuel usage
Eaton prototype vehicle.

Solution
Use MathWorks tools for Model-Based Design to develop a single-shaft parallel hybrid electric powertrain
“Our typical software control projects take two years to create requirements, generate code, and get a vehicle operating in a stable form. We cut that time in half with the help of MathWorks tools.”
Jeffrey Carpenter Eaton Corporation
Link to user story

Results
? Emissions reduced by 90% and fuel economy increased by 50% ? Results seen instantly ? Optimal architecture designed

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