model-based design for motor control development

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Model-Based Design For Motor Control Development

Paul Crook – Analog Devices

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Topics

Developing motor control applications using model-based design and automatic code generation

Hardware platform Model-Based Design overview Modeling of motor control systems Simulation and automatic code generation Running generic C-code on embedded platformWhen to use MBD and when to use “traditional” design

methods

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ADI Demo Platforms

Motor Drive hardware 3 phase PM motor PC with MATLAB®, Simulink®, and IAR Embedded Workbench

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HW Platform

100V-250V AC input with PFC, 5A Isolated current, position, voltage and diagnostic feedback 3-phase, 0.55kW, permanent magnet synchronous motor

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ADSP-CM40x Embedded Target

Key PointsARM CORTEX M4F240MHzTwo 16-bit ADCs

380ns conversion time

SINC filter384kB SRAM2MB Flash20 DMA channelsPeripherals for

motor controlCommunication

interfaces

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MODEL-BASED DESIGN

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Why Model-Based Design?

• Requirements Development• System Simulation• Automatic Code Generation• Continuous Verification

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The Traditional Design Process

Requirements Phase

Design Phase

Realization Phase

Testing Phase

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Traditional Design of a Multi-Domain System

Integration, Test & Certification

Research & Requirements

Software

Requirements

Design

Realization

Testing

Hardware

Requirements

Design

Realization

Testing

Mechanical

Requirements

Design

Realization

Testing

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The Cost of a Traditional Design Process

Source: ROI for IV&V, NASA

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Model-Based Design

• Automate regression testing

• Detect design errors

• Parameter tuning

• Support certification and standards

• Generate efficient code

• Explore and optimize implementation tradeoffs

• Model multi-domain systems

• Explore and optimize system behavior in floating point and fixed point

• Collaborate across teams and continents

INTEGRATION

IMPLEMENTATION

DESIGN

TE

ST

& V

ER

IFIC

AT

ION

RESEARCH REQUIREMENTS

ARM

C, C++

Environment Models

Physical Components

Algorithms

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Model-Based Design for Embedded System Development

Designwith

Simulation

ExecutableSpecifications

ContinuousTest and

Verification

AutomaticCode Generation

Models

Designwith

Simulation

ExecutableSpecifications

ContinuousTest and

Verification

AutomaticCode Generation

ModelsTest with Design- Detects errors earlier

Simulation- Reduces “real” prototypes- Iterative “what-if” analysis

Automatic code generation- Minimizes coding errors

Executable models- Unambiguous- “One Truth”

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Steps in MBD approach

1. Plant modeling- Motor, load, power electronics etc.

2. Interface modeling- Sensors, device drivers

3. Controller modeling- Field oriented control of 3 phase PM motor

4. Analysis and synthesis- The models created in step 1-3 are used to identify dynamic

characteristics of the plant model.- Tuning and configuration of system

5. Validation and test- Offline simulation and/or real-time simulation- Time response of the dynamic system is investigated

6. Deployment to embedded target- Automatic code generation- Test and verification

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Steps in MBD approach

Simulation of Controller and Plant model Off-line development of algorithms without access to HW Code generation and deployment to embedded controller Comparison between simulation and actual implementation

PC

Target

Controller Model

Motor/inverterModel

System Model

TestSignals Verify

CodeGeneration

Motor Drive System

System

Embedded Controller

Motor/inverter

HW

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Model complexity

What is a good model?Represent the states of interestSupports automatic code generationExecute fastUse existing (trusted) librariesReusable across platforms

Too little Too much

?

Right balance

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Model complexity

Understanding strengths and weaknesses of tools is critical when defining model scope

Strength

Weakness

- Solving differential equation- Time domain modeling- Visualization- Target specific setup- Managing system resources- Time scheduling

- Register setup and control- Managing System resources- Time scheduling- Real time control systems- Debugging and test- Visualization

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Interfacing to Embedded Target

SW architectureGeneric C-code needs to be tied to embedded platformUtilizing strength of multiple environmentsGraphical environment for application code development“Classic” C-code for target specific control

Control Algorithm

Embedded Platform

HW interface layerPl

atfor

m

inde

pend

ent

Platf

orm

de

pend

ent

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Interfacing Embedded Target

Two platforms – one implementationCommon control algorithm for Simulation and Embedded systemPlatform specific device drivers or behavioral models

Debug algorithm using simulation and test on embedded platform

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Drive system feedback and control

Feedback

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Motor Control Application Program

Power Inverter and Motor

Sensors, interfaces and Device Drivers

MC Algorithm

MC ‘C’ codeDevice driversApplication code

CC & LinkMC ApplicationFirmware

Device Driver

Aut

oco

de

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AC Motor Algorithm (Field Oriented Control)

Shaft position sensor measures rotor magnet (flux) position – velocity is calculated. Two/three motor currents measured (two is enough). Clarke-Park transfer calculate Torque (Iq) and Flux (Id) components of current. Speed loop generates Iq reference; Id reference set to zero for PMSM. Current loops and Field alignment generates a,b,c voltage commands.

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Field oriented control

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Simulation

0 1000 2000 3000 4000 5000-250

-200

-150

-100

-50

0

Frequency [kHz]

Mag

nitu

de

[dB

]

0 1000 2000 3000 4000 5000-3000

-2000

-1000

0

Frequency [kHz]

Pha

se [

Deg

rees

]

Simulink scopes Code debugging

Data analysis in MATLAB

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Generating C-code

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Building and Linking – IAR EWB

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Building and Linking

Workbench

Matlab/Simulink

Motor Control- FOC functions

ARM Library- M3/M4 support

Executable

C-code

Device drivers- SW enablement package

Application code- State machines

Scheduling- IRQs/RTOS

C-code C-code

Device Drivers- Functional model

System resources- Allocation & setup

Legacy code- Existing code

CM40x

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Running target

MATLAB GUI + embedded engineStreaming data back to MATLAB

0 20 40 60 80 100 120 140 160 180 200-1000

-500

0

500

1000

Dut

y-cy

cle

Captured runtime data

0 20 40 60 80 100 120 140 160 180 2002.5

3

3.5

4x 10

4

Pha

se c

urr

en

t

0 20 40 60 80 100 120 140 160 180 2000

2

4

6

8R

oto

r a

ng

le

Sample number

28

Sampling domains

10 kHz

1 kHz

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Summary

Model-Based DesignSystem modeling and simulationAutomatic code generation

Modeling of motor control systemsElectronics/mechanicalInterfacesControl algorithm

Interfacing generic C-code to embedded targetUse of different environmentsDevice drivers

Debugging and testOff-lineIn real-time

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Questions