2. Fuzzy logic is a new and innovative technology being used to enhance control
engineering solutions.
It allows complex system design directly from engineering experience and
experimental results, thus quickly rendering efficient solutions.
In a joint application project, Texas Instruments and Inform Software have used
fuzzy logic to improve AC induction motor control.
The results were intriguing: control performance has been improved while design
effort has been significantly reduced.
Market analysis shows that 90% of all industrial motor applications use AC
induction type motors.
The reasons for this are high robustness, reliability, low cost, and high efficiency.
The drawback of using an AC induction type motor is its difficult controllability,
which is due to a strong nonlinear behavior stemming from magnetic saturation
effects and a strong temperature dependency of electrical motor parameters.
3. For example, the rotor time constant of an induction motor can change up to 70%
over the temperature range of the motor.
These factors make mathematical modeling of motor control systems difficult.
In real applications, only simplified models are used.
The commonly used control methods are:
– Voltage/frequency control (U/f)
– Stator current flux control (Is/f2)
– Field oriented control
4. Of these approaches, the field-oriented control method has become the defects
standard for speed and position control of AC induction motors.
It delivers the best dynamic behavior and a high robustness under sudden
momentum changes.
Also, due to the strong dependency of the motor’s parameters, a controller
optimized for one temperature may not perform well if the temperature changes.
To avoid the undesirable characteristics of the field oriented control approach, the
companies Texas Instruments and Inform Software have developed new alternative
control methods, and compared them with the field oriented control approach.
The alternative methods involved two types of flux controllers enhanced by fuzzy
logic and Neuro Fuzzy techniques, respectively.
The goal was to use fuzzy logic to improve the dynamic behavior of the flux
control approach such that the robust behavior of the flux controller and the
desirable dynamic properties of the field oriented controller are achieved
simultaneously.
6. Figure shows the principle of field oriented control.
It allows for control of the AC induction motor in the same way a separately
exited DC motor is controlled.
The flux model computes the “phase shift” between rotor flux field and stator
field from the stator currents iu and iv, and the rotor angle position n.
The field oriented variables of the two independent controller units are
subsequently computed by the transformation of the stator currents using this
“phase shift.”
7. The actual control model consists of two components of cascaded standard PI
controllers.
The upper component comprises outer magnetizing current (imR) controller and
inner isd current controller.
The lower component comprises a speed controller and momentum controller.
The input of the speed controller is computed as the difference between set speed
nref and filtered measured speed n.
To optimize the field oriented control model, all controllers must be
parameterized and optimized individually.
In this application project, the method of optimized amplitude adaptation was
used to tune the current controller, and the method of the symmetrical optimum
was used for the velocity controller.
Implementation effort for the field oriented controller was three person months,
including parameterization and design of the flux model.
8. The computation time for the inner current controllers, the flux model, and the
coordinate transformation is 100 μs on a TMS320C31-40MHz digital signal
processor.
When switching the set speed from −1, 000 to +1, 000 rpm, the new set speed is
reached within only 0.25 s without any overshoot.
However, this excellent performance is not always available.
When the motor heats up the control performance drops significantly, and a
motor with slightly different characteristics will achieve only mediocre results
utilizing the same controller.
9. Fuzzy Flux Control Method:
The conventional flux control model has been enhanced by fuzzy logic in two
steps.
In the first step, the nonlinear relation between slip frequency and stator current
was described by a fuzzy logic system (Fuzzy Block #1).
Figure shows the principle of the resulting fuzzy flux controller.
Fig: Principle of fuzzy flux controller
10. The control model consists of three inner control loops and one outer control
loop.
The inner control loops control the three stator phase currents using standard PI
controllers.
The outer control loop determines the slip frequency n2, also using a standard PI
controller.
The slip frequency is the input to Fuzzy Block #1, which outputs the set value of
the stator current.
The primary objective for Fuzzy Block #1 is to keep the magnetizing current
constant in all operating modes.
The magnetizing current is a nonlinear function of the slip frequency, the rotor
time constant, the rotor leakage factor, and a non constant offset current.
The stator frequency n1 is the sum of the measured rotor frequency n and the slip
frequency n2.
11. The reference position is determined by integration of the stator frequency n1.
Modulated by sin/cos, the reference position is multiplied with the set value of the
stator current, and split back into a three phase system of the stator current set
values.
The rules of the fuzzy block were not manually designed, but rather generated
from existing sample data by the Neuro Fuzzy add-on module of the fuzzy TECH
design software.
Neuro Fuzzy utilizes neural network techniques to automatically generate rule
bases and membership functions from sample data.
The benefit of the Neuro Fuzzy approach over the neural net approach is that the
result of Neuro Fuzzy training is a transparent fuzzy logic system that can be
explicitly optimized and verified.
In contrast, the result of a neural net training is a rather nontransparent black box.
12. Comparison with Field Oriented Control:
Figure shows the performance of the fuzzy flux controller in comparison
with the field oriented controller.
Fig: Enhanced fuzzy flux controller
13. The overshoot performance is almost as good as that provided by the field
oriented control, however, it takes the fuzzy flux controller almost twice as long to
reach the new set speed (curve Fuzzy 1 ).
On the other hand, parameterization and optimization of the fuzzy flux controller
only required four person days.
The computation time for the entire controller is 150 μs on the TMS320C31-
40MHz digital signal processor.
To improve the performance of the fuzzy flux controller, in a second step, the
standard PI controller for the outer control loop was replaced by a fuzzy PI controller
(Fuzzy Block #2).
This fuzzy PI controller does not use the proportional (P) and integral (I)
component of the error signal, but rather the differential (D) and proportional (P)
component then integrates the output.
This type of fuzzy PI controller has been used very successfully in a number of
recent applications, especially in the area of speed and temperature control.
14. In contrast to the standard PI controller, the fuzzy PI controller implements a
highly nonlinear transfer characteristic.
The sub window in the lower left part of Figure shows the transfer characteristics
for the fuzzy PI controller implemented in this application.
Fig: Simulation of the enhanced fuzzy flux controller using the software products
fuzzy TECH and Matlab/Simulink
15. The enhanced fuzzy flux controller reveals a much-improved dynamic
performance.
The good performance attained in this case hinges on the nonlinear behavior of
the fuzzy PI controller.
In contrast to the conventional linear PI controller, the nonlinearity of the fuzzy
PI controller produces stronger control action for a large speed error, and a smoother
control action for a small speed error.
This also results a higher robustness of the enhanced fuzzy flux controller against
parameter changes.
The implementation of the second fuzzy block with the fuzzy flux controller only
required an additional day for the fuzzy logic system itself, and two additional days
for the optimization of the total system.
Hence, the total development effort for the enhanced fuzzy flux controller was
seven person days in comparison to three-person month for the field-oriented
controller.
