Testbed For Real-Time Control & Parameter Estimation

NAECON 2019 Dayton, OH10:00 am on 16 July 2019

Presenter:Yingda TaoUniversity of Alabama

Presentation Overview

❏ Project Summary❏ Background, Project Requirements

❏ Overview of System & Subsystems

❏ Subsystem Integration

❏ Integration/System Verification

❏ Concluding Remarks

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Problem DefinitionThe electromagnetic constants of permanent magnet

synchronous motors used in vector control equations are typically determined during manufacturing for each individual motor. However these motor parameters are not actually constant, but have some small variance depending on the context of use.

Therefore, it is beneficial for motor control & power efficiency to have some way to accurately estimate and update these parameters in real-time, thus further optimizing motor performance.

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Requirements ❏ Digital control system & testbed for an internal permanent magnet motor

❏ Design, fabrication, and assembly of all components necessary to integrate off-the-shelf subsystems (microcontroller, data acquisition system, power inverter, PC, dynamometer)❏ PWM Level Shifter PCB❏ Power & Data Cables❏ Motor-Dynamometer coupling, structural housing, stabilization.❏ Simulink Controller models

❏ Feedback data required by the controller from the motor:❏ Rotor frequency (speed, rpm), ωr

❏ Rotor angle(position, degrees), θr

❏ d-q domain stator current measurements & control voltages, id, iq, vd, vq

❏ Software Simulation of System (IPM motor, power electronics, & controller)❏ Parameter Estimation Algorithm implemented as Matlab Functional Blocks

❏ Solution must output estimated dq-domain stator inductance (Ld , Lq ) and stator resistance (Rs ) ❏ Estimates must be updated in real-time via recursion (update at resolution of system, 0.1 ms)

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Deliverables

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❏ Fully-functional experimental testbed for physical motor control and performance analysis in real-time

❏ Simulink models for closed-loop motor control and for software simulation of motor-power electronics system

❏ MATLAB/Simulink parameter estimation algorithm and functional blocks

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Permanent Magnet Motor Features

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❏ High torque and high efficiency ❏ When current flow into the Stator, create a

rotating magnetic field, it rotate at speed N.

❏ Consumes up to 40% less power compared to conventional induction motors.

❏ Since the permanent magnet is centrally embedded, mechanical safety is improved as the magnet will not detach due to centrifugal force.

Image source: Google

IPM Motor Integration

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❏ Four high-power banana cables were soldered to custom motor power cable and plug, for interfacing with IGBT Inverter via the data acquisition system.

❏ DC bus supply 230 dc voltage to Inverter which convert dc voltage to 230 ac voltage to IPM motor

❏ Motor encoder plug connect with dSPACE MicroLabBox which Generates six synchronous PWM signals for control of current to the motor

❏ IPM motor & dynamometer connected and stabilized with custom metal brackets and mounts, with both attached to a metal table.

Dynamometer

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❏ Used to apply load (+/-) to IPM motor❏ Dynamometer has two test modes

❏ Speed mode❏ Torque mode

❏ From the speed mode, we can set speed direct value (in RPM), also we can start the motor in forward or reverse direction

❏ Through the torque mode, we can also set the torque value (in N-m) and monitor torque and frequency

❏ From the screen, we can monitor motor frequency and torque in real-time

Power Electronics Integration

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❏ DC power source is MAGNA-Power Electronics, Input of 3-phase 200 VAC / 175 AAC and output up to 500 VDC/ 100 ADC.

❏ Inverter to change 230 VDC to 230 Vpk-pk AC

and ~5-15 Aac three-phase power to the motor

❏ Level shifter PCB is using TI CD4504B Voltage Level Shifter IC in order to amplify 5V PWM signal to 15V required by inverter

IPM Motor Verification

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❏ Used dSPACE on-board data acquisition system to determine parameter values by measuring time constant, τ, of rotor current transient from a step response as well as stator resistance Rs using ohm meter.

❏ Input τ and Rs to the equation Ld/q = (⅔) τd/qRs ,to get the numerical value of Ld and Lq in mH. Repeated multiple times to get a average value for both parameters. Additional parameters found in motor datasheet.

❏ dSPACE and Dynamometer were connected with IPM motor to verify speed and position measurements with digital feedback from motor encoder speed and position data.

Power Electronics Integration

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❏ High-power banana cables help connect the inverter to the DC power supply and inverter power output to motor via the DAS.

❏ On the PCB, a 9-pin connector carries 6 PWM signals at 5V from the dSPACE to the level-shifter

❏ Six BNC cables carry the amplified (15V) PWM signals from the level sifter to the inverter

Power Electronics Verification

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❏ Before PCB construction, schematic was simulated using PSpice

❏ The Level Shifter component was tested on the breadboard before PCB was designed and ordered

❏ Power supply, oscilloscope, and function generator were used to test the Level Shifter PCB.

❏ Testing showed it properly amplified PWM signal at high frequency (>20 KHz)

Control System Integration

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❏ dSPACE MicroLabBox generates six PWM

signals for control of current motor.

PWM cable connects dSPACE to power

electronics.

❏ Data acquisition system measures current

from the motor to the dSPACE via three

banana plugs

❏ Speed and positioning data of motor sent

to dSPACE via encoder cable.

IPM Testbed Control System Model – Simulink

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Testbed Controller Diagram with Parameter Estimation

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ControlDesk Real-Time Interface for dSPACE

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Simulink Simulation and Verification Model

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Simulation

vs.

Testbed

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Simulation vs. Testbed

Recursive Least Squares Parameter Estimation

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Least Squares

LS Recursive Algorithm

Source: H. Neugebauer [1]

Estimated Parameters

Regression Equation

Parameter Estimation Block in Controller Diagram

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Estimation of IPM Stator Resistance, RsSimulation vs. Testbed

Estimation of IPM Stator d-axis Inductance, LdSimulation vs. Testbed

Testbed

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Estimation of IPM Stator q-axis Inductance, LqSimulation vs. Testbed

External ImpactsProject motivation is for application in Electrical planes and electric vehicle

(EV) drives

❏ Electrical planes/Electrical vehicle: ❏ 5 Categories of Electric Planes: eVTOL, Flying Taxi, Passenger/Cargo Drone,

Passenger PlanesHybrid Aircrafts

❏ IPM have higher efficiency compared to combustion engine: ~90% v.s.

~20% energy efficiency

❏ Energy-saving and environmental protection, high efficiency and low energy

consumption, near zero emission, low noise and vibration leve

❏ Safety and reliability (no explosion and fuel leakage), simple structure, easy

operation and use, good maintenance/low cost

❏ Technic Constrains: Battery, CapacityDesign SpaceThermal, Management for

Motors (i.e. PMSM), Power Control Integration with FBW System

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Need to increase the efficiency of the motorsAccurate the rotation of motors precisely Simplify the E-Power Control System

Aerospace ApplicationSolution

🞐🞐 Real-time detection of output efficiency🞐🞐 Collection🞐🞐 Resistance

🞐🞐 Ld/ Lq

🞐🞐 Current

🞐🞐 Voltage

🞐🞐 Flux

• PMSM is always operating in identical conditions to ensure that the parameters are not changing continuously. • One must determine the motor parameters in real-time and adapt the controller accordingly

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Further Potential

❏ Make parameter estimation “closed-loop” Integrate

inductance control with PID control—— Close Loop Feedback

● To increase the flight stability by modifying instantly Rotating speed of

E-Motor-Propeller

❏ Combine the power control to FBW control

● To decrease the flight vibration by link-ageing the rotation status of

each motors with the real-time deflection of control surfaces

❏ Make system truly embedded and housed in single unit

❏ Enable estimation only during steady state operation

❏ Test & use in real dynamic load applications

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Motors

InductanceControl

Feedback signal

Acknowledgements & References

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● Dr. Shuhui Li - Project Supervisor

● Yang Sun and Haoyang - Graduate Student

● ECE Department at University of Alabama

● NAECON 2019

[1]

H. Neugebauer, “Parameter Identification of a Permanent Magnet Synchronous Motor,”

Master’s, Chalmers University of Technology, Gothenburg, Sweden, 2012.

Note: All images came from google images or team members unless otherwise specified