State of the Art of Induction Motor Control

State of the Art of Induction Motor Control
Joachim Böcker, Member, IEEE, Shashidhar Mathapati
University Paderborn, Warburger Str. 100
D-33098 Paderborn, Germany
Abstract—The induction motor is well known as the
workhorse of industry. The development of variable speed
induction motor drives has a long history of more than four
decades. Today’s sophisticated industrial drives are the result of
the extensive research and development during the last decades.
In this paper the historical and recent developments and major
milestones in control of induction motors are pointed out first
and second how research results were translated into today’s
industrial standards, and third at last, what are the current
trends in research and industry are summarized.
B
Index Terms—Induction motor control
I. INTRODUCTION
EFORE the invention of variable frequency voltage and
current source inverters the induction motor was never
thought as continuously variable speed drive. Only some
adaptations of the load characteristic were feasible by
manipulations of the rotor resistance. The early days of
variable speed induction motor drives can be recorded back to
the 1960s, supplied by the silicon controlled rectifier (SCR).
In that time the principle of speed control was based on steady
state considerations of the induction machine. The v/f control
was one outcome and even today it is commonly used for the
open-loop speed control of drives with low dynamic
requirements. Along with that, another well known control
technique was the slip frequency control method that was well
known for to yield better dynamics. This method was adopted
in all high performance induction machine drives until fieldoriented
control (FOC) became the industry’s standard for AC
drives with dynamics close to that of DC motor drives. The so
called vector control or the field-oriented control was one of
the most important innovations in AC motor drives which
opened the door for the researchers aiming for ever enhancing
control performance. This contribution is organized as
follows, control aspects of induction motor drives are
discussed in Section II, aspects on observer theory are
discussed in Section III, sensorless drives in Section IV, and
parameter identification in Section V. The industrial standards
of induction motor drives is outlined in Section VI and
followed by the future trend and needs in section VII.
*
ω rs
−
ω rs
*
i sd
*
i sq
Steadystate
1 i
w
r
=
T i
r
VoltageModel
*
sq
*
sd
*
u sd
*
u sq
j r
e − θ
d, q
α,β
θ r
∫
*
ω r
PWM
ω rs
Speed
Sa
Sb
Sc
U dc
Inverter
Motor
* i ψ
−
i ψ
+
E VR1
sinψ
−
VR2
cosψ
U
VA
i s
U β
ψ
U α
(a)
Sensor
(b)
*
ω rs
−
ω rs
* ψ r
−
ψ r
*
i sq
−
î sq
*
i sd
î sd
−
VFF
VFF
d,q
α,β
*
u sα
*
u sβ
PWM
Sa
Sb
Sc
U dc
Inverter
d,q
i s
θ r
Observer
a , b,
c
u s
ω rs
Motor
(c)
Fig. 1. Different field-orientated control schemes
1-4244-0743-5/07/$20.00 ©2007 IEEE
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*
ψ
s
* ψ sa
−
ψ sa
ψ sb
*
* ψ sc
−
ψ sb
−
ψ sc
Sc
Sb
Sa
U dc
Inverter
*
ψ
s
*
T
−
T
−
ψ
s
Switching
table
Sa
Sb
Sc
U dc
Inverter
T *
T
−
a , b,
c
α, β
ψ
s
Flux
Torque
u s
Torque , Flux,
Sector
Estimation
DSC
Motor
DTC
Motor
Fig. 2. DSC and DTC block schematics
II. CONTROL APPROACHES
Though R. H. Park [1] introduced rotating reference frames
already in 1929, it took a long time to develop the idea of
field-oriented control (FOC) that is based on the fundamental
insight that the torque is proportional to the cross product of
stator current and flux i
s
× ψ . The resulting decoupled
r
control of torque and field excitation is then quite similar to
DC motor control. Roots of FOC started from Germany. As
one of the first, Hannakam [2] built up a dynamic model of
induction machine with analog computer in 1959. Then in
1964, Pfaff [4] studied the dynamics of induction motors with
variable frequency supply. These publications in conjunction
with the text book of Kovacs and Racz [3] were then the
building blocks for the concept of Indirect FOC (IFOC)
presented by Hasse in 1968 [5][35]. Later in 1971, Direct
FOC (DFOC) was developed within Siemens by Blaschke [6].
Both authors proposed an orientation aligned with the rotor
flux vector. In the mid 1980s, when many researchers worked
on improvements of the basic FOC, Depenbrock presented the
Direct Self Control (DSC) [17] and Takahashi and Noguchi
the Direct Torque Control (DTC) [16]. Unlike FOC which
includes pulse width-modulated current control loops, DSC
and DTC are hysteresis controls working directly with stator
flux and torque without having the need for an inner current
control loop.
A. Field-Oriented Control (FOC)
In the concept of Indirect FOC (IFOC), flux orientation is
realized only by means of feed-forward control, typically by a
calculation of the slip frequency from the reference values,
Fig. 1a. This approach is simple and well performing for the
speed and position control even at low speeds. However, the
major drawback is that, the orientation of the control is very
sensitive to the rotor resistance, which affects the robustness
of the control. To overcome this problem the rotor resistance
has to be estimated online [12][14].
The other way is the Direct FOC (DFOC). The original
approach of Blaschke [6] included flux measurement coils to
accomplish the flux orientation, Fig. 1b. Instead of flux
measurement, DFOC includes usually flux observers to enable
flux orientation, Fig. 1c. The different types of flux observers
will be treated in the next section.
The concept of field orientation is not restricted to rotor
flux orientation but also possible with stator or air-gap flux
[18]. In the late 1980s there were few publications on stator
flux orientation [19] that presented some advantages over the
rotor flux-oriented control. A generalization is the concept of
universal field orientation [27].
B. Direct Self Control and Direct Torque Control
Unlike FOC with stator current as inner control objective,
DSC and DTC govern the stator flux by means of hysteresis
controls. The overall concept of torque control in DSC [17]
and the DTC [16] is the same, block schematic of these
controls are shown in Fig. 2. The difference between the
techniques is that DSC performs a hexagonal flux trajectory
while that of DTC is circular. The DSC was developed for
high power and traction application. Both possess high torque
dynamics compared to FOC. However, both the control
techniques have the inherent drawbacks of variable switching
frequency and higher torque ripple. Since then it has been
continuously worked by researchers to overcome these
inherent drawbacks. These problems opened various roots for
researchers to work on different kinds of strategies to avoid
the variable switching frequency [22][24][37], but sticking to
the fundamental concept of torque control.
In order to realize the control, either DSC or DTC, flux and
torque estimates have to be provided by a flux observer, quite
similar to FOC. Although, mostly the so-called voltage model
is mentioned in combination with DTC this is not mandatory,
as the control will also run with other types of observers.
III. FLUX AND TORQUE OBSERVERS
Task of the observer is to provide estimates of the flux of
the motor using available measured signals such as current,
voltage and speed. Depending on the control structure (FOC,
DSC, DTC) either stator or rotor flux estimates are needed.
However, since the rotor flux can be calculated effectively
from the stator flux (and vice versa) as long as the current is
available, this does not affect the presented main principles.
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u s
is
Lr
Lm
RsLr
Lm
LsLr
σ
Lm
−
−
ω 0
1
P
(a)
−
ψ
r
i s
Lm
Rr
Lr
ω rs
−
(b)
1
P
Rr
− jωrs
Lr
ψ r
u s
−
−
1
: Integrator
P
2
Lm
σ = 1−
LsLr
1
P
Rs
σLs
ψ
s
Rr
σLsLr
− RsLm
σLsLr
(c)
ω rs
−
1
P
Rr
− j
Lr
ω
ψ
r
rs
i s
u s
Rs + σLsP
ω rs
−
(d)
ψ
2
Lr r
Lm(
Rr − jω rs Lr)
Fig. 3. Open-loop observers: a) Voltage/stator model b) Current/Rotor model
c) Voltage and speed model d) Voltage current and speed model
u s
B
ω rs
K
A( ω r
)
ψˆ
r
C
i s
− î s
u s
i s
ω rs
1
P
j r
e − θ
Lm
1 + τ rP
j r
e θ
Current
Model
−
Kp
Ki
P
Rs
−
1
P
σLs
−
Voltage
Model
ψˆ
r
(a)
(b)
Fig. 4. Closed-loop observers: a) Luenberger type b) Gopinath type
Once a flux estimate is available, also a torque estimate can
easily be computed. Thus, all the flux observers can also
provide torque estimates.
A. Open-Loop observers
Open-loop observers do not include measures of error
feedback. They result in a straight-forward way from the
motor modeling.
Voltage or Stator Model (inherently speed sensorless): The
first flux estimator proposed in early 1970s [7] used for the
FOC was based on the stator circuit. The estimator is a simple
integrator calculating the stator flux vector from
ψ =
∫(
u
s
− Rsi
s
) dt , and from that the rotor flux. The
s
method is very sensitive to offsets (such as motor current) due
to the pure integration. The other drawback is erroneous
estimation at low speeds, since the temperature dependent
drop of R sis
dominates over the motor terminal voltage. In
order to overcome the offset problem, a low pass filter (LPF)
for the flux estimation is utilized in [14], and shown in Fig.
3a. It avoids the estimator windup but restricts to limit the low
speed operation much above the cut off frequency of LPF.
However, this model works very well at higher speeds and
field weakening [19].
Current or Rotor Model: Almost at the same time in 1974
the DFOC presented in [8] with the rotor model, which is also
well known as current model. The performance of this current
model is sensitive to the rotor resistance at higher slips [20],
[28], shown in Fig. 3b. However, there is no particular
problem for operation at low speeds.
Voltage-Current and Speed Model: This model in the same
publication [8] proposed, and the details of implementation
can be seen in [13]. This is also known as open loop estimator
with cancellation technique, this technique suffers from many
issues like differentiation of current, division by speed and an
inherently high parameter sensitivity [28]. The model is also
shown in Fig. 3d.
Voltage-Speed Model: This is a full order flux observer,
which resembles the electrical model of the motor. The
observer uses the motor voltage and speed and calculates the
stator and rotor fluxes, shown in Fig 3c. The utilization of
such a model for the control can be found in [13].
The detailed error analysis due to the parameter variation
for the above estimators can be found in the doctorate thesis
of Zägelein [15].
B. Closed-Loop observers
The well known closed-loop observers are based on
Luenberger type observer and Gopinath’s type observer. Both
types allow an error feedback.
Luenberger Observer: A full-order observer is based on the
Voltage-Speed model of the motor, where motor current is the
output quantity of the state space model. This estimated
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Sensorless
control
Fundamental wave
model
Exploited
anisotropies
Voltage
model[38]
- 2% of rated speed
- Error of 0.5 rated slip
Open loop
observer
- ≥ 2Hz
MRAS
[25]
- Error of 0.5 rated slip
Full order
non - linear [26 33]
- 0.02 pu [33]
- Error of 0.5 rated slip
Closed loop
observer
Sliding mode
[36]
- 0.002 pu with load
- Error of 0.5 rated slip
Rotor slot
Mag - inductance
saturation
Artificial
sailiency
Fig. 5. Different sensorless control schemes
current is used to compare with the actual measured current
and drive the error in the feedback path to improve the
performance of the observer. Such a structure is shown in Fig.
4a. The utilization of such a scheme can be traced back to the
early 1980s in [15]. In this thesis, the author has provided in
depth for error analysis with the parameter variations and also
presented some simple optimization techniques for the
feedback circuit to minimize the flux magnitude and phase
errors. Researchers further explored this type of observer with
the stochastic approach (based on Kalman Theory) [23].
Gopinath’s type observer: The closed-loop flux observer is
formed from the two most desirable open-loop estimators
(presented above) which are referred as voltage model and
current model, Fig. 4b. The smooth transition between these
two models is governed by the bandwidth [29], which is
determined by the proportional and integral gains K P, K I of
the system.
IV. SPEED SENSORLESS CONTROL
Speed and position sensors are undesired due to various
reasons as cost, cabling, robustness, and construction
constraints. Many different approaches have been presented
for speed sensorless induction motor control, e.g. [25], [26],
[38].
Methods which are based on models assuming sinusoidal
field distribution belong to the class of fundamental-wave
methods. Many approaches of this class are derived from
classical motor modeling or are originating from system
identification techniques. All these methods work more or less
reliably for medium to higher speeds. The principle problem
with fundamental-wave speed estimation is that the induction
motor becomes an unobservable system at zero stator
frequency. Many efforts were spent to reduce the practical
margin of minimum speed or to avoid zero frequency by
appropriate manipulations of the flux magnitude.
If zero frequency is an issue, spatial anisotropies
(saliencies) of the motor like magnetic saturation or slot
harmonics could be utilized to detect speed and position of the
rotor or the flux, respectively. Two main streams were
presented working either with injection of additional
harmonics [31] or applying step-like test signals [30].
However, exploitation of anisotropies of induction motors is
much harder compared to synchronous motors, since natural
saliencies of normal induction motors are very small due to
skewing and optimized numbers of stator and rotor slots.
Thus, motors with particular properties and individual tuning
were required. Fig. 5 shows an overview about the various
methods of speed sensorless control [38].
V. PARAMETER IDENTIFICATION
Sophisticated model-based controls as FCO or DTC need
precise information about the motor parameters like stator,
rotor and mutual inductances and stator and rotor resistances.
A lot of parameter identification techniques have been
proposed, which can be grouped as off-line and on-line
identification. Off-line identification takes place during
commissioning or turning-on, while on-line identification is
carried out during the normal operation. First proposals for
off-line identification and self-commissioning are presented in
the 1980 [21]. Up to today there are multitudes of publications
and text books on parameter identification. Normally, off-line
identification has to be done at standstill, because usually the
shaft is not allowed to rotate.
VI. INDUSTRIAL STANDARDS IN INDUCTION MOTOR DRIVES
Today, three-phase induction motor drives are employed in
different industrial areas with a wide power range starting
from few 100W to several MW. Drives industry is very
thankful to the present generation of powerful
microprocessors, which is responsible for the realization of
control functions within short cost margins. However, even
today, cost of controller hardware is a limiting constraint,
particularly at the low-power and low performance drives.
Main market share of about 80-90% are simple drives with
low dynamic requirements like pumps and fans. All these
drives are working without speed sensors. The control
principle is still based on v/f control. In this segment there is
no need to introduce more powerful controls.
More sophisticated control is needed for all kinds of drives
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that require high dynamic speed or torque regulation or high
torque accuracy. Some examples are elevators, cranes, tooling
machines and many kinds of industrial automation drives.
Even drives for steel and paper mills, though it seems they are
only continuously running, demand highest performance due
to the extreme quality of the technologies. For most of these
applications industrial standard products are available that
come either with FOC or DTC. Many results from research
have found their way to these industrial products.
However, it has to be distinguished between standard offthe-shelf
products and those that are individually designed or
adapted for particular applications, typically in the high power
range.
Installation and commissioning of standard drives is often
done by technical staff that is not familiar with the details of
AC motor control. Thus, higher level methods as Luenberger
observer or extended Kalman filter, requiring special
education are usually not adopted. These controls are results
of compromises between performance, cost, and, not least,
easy usability.
Self-commissioning functions are growing. However, that
is often done not by means of parameter identification
techniques, but using databases with motor data of the
supplier’s list.
Individually designed controls, e.g. for traction drives and
large industrial drives is often much more sophisticated and
close to latest research. In that business, an experienced staff
is usually responsible for development as well as for
commissioning.
Though most industrial controls allow also speed sensorless
operation, typically degradations in performance have to be
accepted. Highly performing sensorless drives with dynamic
and precise torque tracking even at low speed and during
regenerative braking are only a very small niche, e.g. when it
is difficult to mount a speed sensor in a wheel hub drive.
VII. FUTURE TRENDS AND NEEDS
Today’s industrial induction motor drives have matured to a
relatively high level compared with needs. To accomplish that
level it took about a decade or more to transfer research
results to today’s industrial standards. However, what are the
open or upcoming issues and future trends?
• Reliable self-commissioning will become more and more
mandatory.
• Depending on the preceding item, market share of vector
controls as FOC/DSC will grow compared to v/f control.
• Servo-type drives seem to be of decreasing importance,
because this area is captured more and more by permanent
magnet synchronous motors. However:
• Because induction motors possess low inertia and are free
of cogging torque, there is a growing market segment of
high speed and test stand drives requiring smoothest
stationary torque, but also capability of rapid torque and
speed changes in order to apply desired test profiles.
• Safety aspects are getting more important. That addresses
software development standards as already partly
introduced as well as operational measures like
redundancy or fallback operation.
• Efficiency-optimized operation will grow more importance
with respect to energy saving demands.
• Controller hardware which is based today on microprocessors
or DSP may change in the future more and
more towards ASICs or FPGA. A growing number of
contributions are observed in this area.
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