ABSTRACT
Model
Reference Adaptive System (MRAS) based techniques are one of the best methods
to estimate the rotor speed due to its performance and straightforward
stability approach. These techniques use two different models (the reference
model and the adjustable model) which have made the speed estimation a reliable
scheme especially when the motor parameters are poorly known or having large variations.
The scheme uses the error vector from the comparison of both models as the
feedback for speed estimation. Depending on the type of tuning signal driving the
adaptation mechanism, there could be a number of schemes available such as
rotor flux based MRAS, back e.m.f based MRAS, reactive power based MRAS and
artificial neural network based MRAS. All these schemes have their own trends and
tradeoffs. In this paper, the performance of the rotor flux based MRAS (RF-MRAS)
and back e.m.f based MRAS (BEMF-MRAS) for estimating the rotor speed was studied.
Both schemes use the stator equation and rotor equation as the reference model
and the adjustable model respectively. The output error from both models is
tuned using a PI controller yielding the estimated rotor speed. The dynamic response
of the RF-MRAS and BEMF-MRAS sensorless speed estimation is examined in order
to evaluate the performance of each scheme.
KEYWORDS
1. BEMF-MRAS
2. MRAS
3. Parameter
Variations
4. RFMRAS
5. Sensorless
Speed
6. Tracking Capability.
SOFTWARE: MATLAB/SIMULINK
BLOCK DIAGRAM
Fig.
1. Basic configuration of MRAS-based speed sensorless estimation
scheme.
Fig. 2. Block diagram of RF-MRAS scheme
Fig. 3. Block diagram of BEMF-MRAS scheme.
SIMULATION
RESULTS
Fig.
4. RF-MRAS estimator's tracking performance at reference speed (a) 100rad/s,
(b) 70rad/s and (c) 50rad/s (d) 30rad/s.
Fig.
5. Effect of incorrect setting of RS values to the RF-MRAS estimator's speed response.
(a) Rs (b) Rsnew = 1.1 Rs (C) Rsnew = 1.5 Rs (d) Rsnew = 2 RS.
Fig. 6. BEMF-MRAS estimator's tracking performance at
reference speed (a) 100rad/s, (b) 70rad/s and (c) 50rad/s (d) 30rad/s.
Fig.
7. Effect of incorrect setting of Rs values to the BEMF-MRAS estimator's speed response.
(a) Rs (b) Rs,ew = 1.1 Rs (c) Rs,ew = 1.5 Rs (d) Rs,ew = 2 Rs.
CONCLUSION
Performance
of RF-MRAS and BEMF-MRAS estimators based on the tracking capability and parameter
sensitivity was presented. The result shows that the BEMFMRAS estimator is more
superior to the RF-MRAS estimator at that particular defined range of reference
speeds. This is prior to the elimination of pure integrators used in the
RF-MRAS scheme. However, the BEMF-MRAS estimator is more difficult to design
due to the non-linear effect of the adaptation gain constants. Therefore, as a whole,
considering all the key criteria of comparison, it can be concluded that the
BEMF-MRAS scheme embrace the requirement as a versatile estimator. It demonstrate
good tracking capability and superb in insensitivity to parameter variations.
REFERENCES
[1]
M. Ta-Cao, Y. Hori and T. Uchida, "MRAS-based speed sensorless control for
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[2]
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[3]
C. Shauder, "Adaptive speed identification for vector control of induction
motor without rotational transducers", IEEE Trans. Ind. Application, Vol. 28,
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[4]
Y.P. Landau, "Adaptive Control: The model reference approach", Marcel
Dekker, New York, 1979.
[5]
M.N. Marwali, A. Kehyani, "A comparative study of rotor flux based MRAS and
back e.m.f based MRAS speed estimators for speed sensorless vector control of
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