Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 1 (June 2017) 106–109___________________________________________________________________________________________________________A Sensor-less Ultra-high-Speed Motor Driver
Chung-Wen Hung*, Yan-Ting Yu, Bo-Kai Huang and Wei-Lung Mao

Department of Electrical, National Yunlin University of Science and Technology
123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, R.O.C.
*wenhung@yuntech.edu.tw
www.yuntech.edu.tw

Abstract
Ultra-high-speed motors play a very important role in machine tools. A speed-sensor-less ultra-high-speed motor
driver is proposed in this paper. Without any speed sensor that is extremely expensive, the proposed driver
employing a sensor-less estimation scheme can perform a closed-loop control very accurately and efficiently. The
design and implementation of the driver and feedback circuits are detailed in this paper. Experimental results showthat the proposed system functions very well.
Keywords: Ultra-high-Speed Motor, Speed-sensor-less Control, Motor Driver, VVVF Control

slip frequency compensation method to decrease thespeed error. Ref. [5] discussed two vector control
methods: field orientation control (FOC) and directtorque control (DTC). The torque responses of bothcontrols are better than those of scalar control methods.
For speed estimation, ref. [6] presented two methods:
the model reference adaptive system (MRAS) and theleast square method (LSM). The former may be used fora wider speed range but is too sensitive to motor
parameters, whereas the latter that calculates the speed
by iteration is suitable only for a narrow speed range. In
this paper, an easier speed estimation method isproposed. The motor slip speed is calculated using themotor model, and then the motor speed is estimated bysubtracting the slip speed from the induction magnetic
field speed. Without any expensive ultra-high-speed
sensor, the proposed driver performed a closed-loop
control very accurately and efficiently.

1. Introduction
The ultra-high-speed motor is an important part ofmachine tools, and most of these motors are induction
motors (IMs) because induction motors are highly
reliable and relatively cheap. In the past, there havebeen many studies discussing various driver methods,
including variable-voltage variable-frequency (VVVF)
control, vector control, proportional-integrated (PI)control, fuzzy control, and neural-network control.
However, these studies dealt with only normal speed
motors, and relatively fewer studies in the literature
have discussed driver methods for ultra-high-speed
motors [1]-[6]. In [1], the driver efficiency wasoptimized using the total harmonic distortion (THD) of
the motor current with auto-adjust driver voltage. In [2],an ultra-high-speed soft switch inverter was developed
to improve the driver efficiency. Both [1] and [2] haveproposed new driver methods.
Although conventional IM driver methods, such as
open-loop scalar control and VVVF control, are popular
because they are relatively easy to implement, somestudies have started using closed-loop control. Ref. [3]employed fuzzy rules to determine the VVVF commandand to increase the driver efficiency. Ref. [4] proposed a

2. Slip speed estimation
The slip speed can be calculated from the so-called flux
orientation control (FOC) method. The motor model canbe simplified by referring to the rotor flux. The
derivation is described as (1) - (5). The equation for
calculating the slip speed, (4), can be derived by setting

Copyright © 2017, the Authors. Published by Atlantis Press.
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
106

Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 1 (June 2017) 106–109___________________________________________________________________________________________________________

zero the flux along the q-axis in (3). In (4), there is avariable flux along the d-axis. It can be calculated by (5)using the current on d-axis.

in Fig 1. The θφr is the angle of the φ in (6). In Fig 1,r(4) and (5) are performed to estimate the slip speed andthe rotor flux on d-axis.

rrr

The block diagram of the slip speed estimator is shownφr = Lm i s + Lr i r

� (1)

1rrr
(φr − Lm i s )→ ir =Lrrr d rr
Vr = 0 = Rr i r + φr + jω sl φr
dt
Rr Lr
φr = m Rr i rs − ( r + jω sl )φrdt
LrLrdrφ dr =dt

LmLrr
Rr ids −RrLrrr

φ dr + ω slφ qrLR rrrr
φ qr = m Rr i qs − r φ qr − ω slφ drLrLrdtdLR rrrr
φ qr = m Rr i qs − r φ qr − ω slφ drddt

Lr→0=Lm
LrLrrr
Rr i qs − ω slφ drLR rrr
r
φ dr = m Rr ids − r φ dr + ω φ qrslLrdtd→LrLR rrr
φ dr = m Rr ids − r φ drLrr
φ dr
Lm→ r =Lidsr s +1Rrdtwherer
φr ：r

φ qr ：Lrrotor flux referredrotor fieldrotor flux of the qaxis referred rotor
field

Lm ：

mutual inductanceω sl ：

slip speedr
is ：r
ids ：

stator current
referred rotor fieldstator current
referred d-axis
refer rotor field

� (2)
⎫⎪
⎬
⎪
⎭
⎫⎪
⎪
⎬
⎪
⎪

r
Lm Rr i qs
→ ω sl =r
Lr φ drdVs
Isd→

ωe

⎬
⎪
⎪
⎪
⎭

Rr ：

a-b-c axis tod-θ axis

ωr

±r
fdrr
ids

Calculate the rotor
flux on d-axisdL
LmI )dt
φ = ∫ r (V − Rs I − (1 − ()) Lssssr

whereφr ：is ：Lmrotor flux
stator current

Ls Lr

Vs ：
Ls ：dt(6)stator voltage
stator inductance

3. Scalar control
In scalar control, the motor is driven by a suitablewaveform. In Fig 2, the curve of speed versus voltage
for a 200 krpm motor is provided by the motor
manufacturer. To drive motor in 30 krpm, the inverter
should provide a three-phase sinusoidal wave with 57
volts and 500 Hz. However, the real rotor speed isdifferent from the driving speed due to the slip speed.
Therefore, a PI controller is used in the proposed system
to compensate the speed error caused by the slip speed.
As shown in Fig 3, the input of controller is the speed
error, and the output is the index of the VVVF table toset the inverter.

(4)

(5)

4. Motor parameter measurement
The IM’s electric parameters are a key in slip speed
estimation. Three parameter measuring tests are used inthis paper. First, DC test is used to measure the stator
resistor. Second, stall test could measure the sum ofstator and rotor resistor, and the sum of stator and rotor
leakage inductance. Then the rotor resistor can beobtained, and the stator and rotor leakage inductances,
𝐿𝑙𝑙 and 𝐿𝑙𝑙 , are assumed equal. Third, no load test isadopted for the stator inductance, and then the mutualinductance could be derived. A virtual stall test isadopted, because a traditional stall test is not convenient
due to extra stalling equivalent. Fig 4 is the wiring
diagram of virtual stall test.

rotor resistor

Lr ：rotor inductances

riqs ：

θfr

ωslip

Fig 1. Block of slip speed estimator(3)

rotor flux of the daxis referred rotorfield

r
Vr ：
r
ir ：

Slip speed
calculating

2φ dr ：

r

IaIb⎭
⎫⎪
⎪
⎪

Calculate therotor flux angleriθsrotor voltage
referred rotor fieldrotor current
referred rotor fieldstator current
referred q-axis
refer rotor field

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Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 1 (June 2017) 106–109___________________________________________________________________________________________________________

control, and generate driving signals for the inverter
circuit. The inverter circuit converts DC voltage to AC
driving waveform.
ωr*ωeSlip speed
calculating

VVVFtableV
freq

Inverter
TerminalM

Bandpassfilterr
I ds

SCI

ePWM

7Switchdriver

MCU

Inverter

SixSwitchesM

DC BUS
24ADC

Fig 3. Block of scalar control
AIsqfrVd cVoltage andcurrentfeedback

a-b-c to d-q

ωr

P1

ACsource

Vs

Angle of rotor
magnetic fieldcalculating

I a Ib

Fig 5. Control block diagram of system

Fig 2. Speed-Voltage characteristic curve of 200krpm motor
ωr'r
I qsr
fdr

Current tomagnetic field

Speedcontroller

MDriverSωitch-Stateωslip±

V & freq

VVVF tableωr±

ωr*ωr'

Speed cntroller±FeedbackcircuitFig 6. System architecture diagram

MFig 4. Wiring diagram of virtual stall test

Two ultra-high-speed motors are installed inexperiments. The parameters of 200 krpm and 300 krpm
motors are measured and shown in table 1.
Table 1. Parameters of 200 krpm and 300 krpm motor
Motor𝑅𝑆
𝐿𝑙𝑙
𝑅𝑟
𝐿𝑙𝑙
𝐿𝑚
200k
1Ω
0.43mH 2.84Ω 0.43mH 5.22mH
300k
1.78Ω 0.21mH 4.33Ω 0.21mH 2.99mH

Fig 7. System photo

5. System architecture and hardware
implementation

Fig 7 is the photo of the system proposed in this paper.
The component at the upper left corner isTMS320F28335; at the lower left corner, an isolated
power board; at the center, an inverter; at the lower
middle, the current feedback circuit; and at the lower
right corner, the DC BUS feedback circuit.

The control block diagram of system is shown in Fig 5.
Scalar control is used in the speed controller. A PIcontroller is adopted to create the speed command
which is the index of the voltage-frequency curve. Here,a lookup table and the interpolation method are used toindex the suitable voltage and frequency commands forthe inverter. And, the feedback speed is calculated byspeed of synchronization field and the slip speed
computed from current and voltage.
Fig 6 presents the system architecture diagram. The
feedback circuit is used to sense the motor current andDC BUS states for speed estimation. The
microcontroller is applied in speed estimation and speed

6. Experimental results
Two experimental results are shown in this paper: stepand ramp acceleration. The experimental power supply
rating is limited, so the step range and ramp accelerationof the speed are limited. Fig 8 shows the experimental
results under 100 krpm ramp command.

Table 2. Step and ramp acceleration of 200 krpm motor
Speed command(rpm)Improved speed error (rpm)Percentage error(%)
Improved stability time(s)

40000
44
0.11
0.281

Step
50000
94
0.18
0.228

60000
132
0.22
X

108

100000
940
0.94
0.34

Ramp acceleration
120000
1600
1.33
0.9

160000
4900
3.06
1.43

Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 1 (June 2017) 106–109___________________________________________________________________________________________________________MOST 105-2221-E-224-024, 105-2218-E-150-004 and104-2622-E-224-016 -CC3.

Fig 8. The experimental results of 100krpm ramp

The experimental results are shown in table 2, and theimproved data of the proposed controller are comparedwith an open-loop driver. The speed error is reduced
and proportional to speed command, and then stability
time is improved in 0.2 to 1 seconds. Note that theexperimental results of the 60 krpm step for theproposed controller are not shown in this table because
the required inrush current is over the capability ofpower supply.
In order to verify the proposed method is suitable fordifferent motors, a motor rated 300 krpm is adopted in
the experiment. The speed errors of different commands
in 200 and 300 krpm motors are shown in Fig 9 and Fig
10, respectively. Though the errors of the 300 krpm
motor are bigger than those of the 200 krpm motor, but
the trends in these two figures are the same. In addition,
the error rate of the 200 krpm motor is lower than 1.5%,
while that of the 300 krpm motor is lower than 3.5%.

Fig 9. The error between estimated and measurement speed of200krpm motor

Fig 10. The error between estimated and measurement speedof 300 krpm motor

Reference
1.

7. Conclusion

2.

As shown in experimental results, the motor with theproposed ultra-high-speed motor driver can achieve thestable state very fast. For practical purpose, theacceptable speed error ratio should be lower than 10%.
The proposed speed estimator can effectively estimate
the motor speed and react the load fluctuation. Even
though the speed error is proportional to speedcommand, the accomplished speed error rate is lower
than 3.5%, which is considered very small. Closed-loop
controller actually improves the system responding timethrough the low-cost feedback circuit and reduces thesensitivity of motor parameters as compared with openloop system. The controller proposed in this paper isalso suitable for different motors.

3.

4.

5.

Acknowledgment:

6.

This work is partially supported by the Ministry ofScience and Technology, ROC, under contract No.

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