ABSTRACT:
Fuzzy logic control has
become an important methodology in control engineering. The paper proposes a
Fuzzy Logic Controller (FLC) for controlling a speed of SRM drive. The
objective of this work is to compare the operation of P& PI based conventional
controller and Artificial Intelligence (AI) based fuzzy logic controller to
highlight the performances of the effective controller. The present work
concentrates on the design of a fuzzy logic controller for SRM speed control.
The result of applying fuzzy logic controller to a SRM drive gives the best
performance and high robustness than a conventional P & PI controller. Simulation
is carried out using matlab simulink.
KEYWORDS:
1.
P Controller
2.
PI Controller and Fuzzy Logic Controller
3.
Switched Reluctance Motor
SOFTWARE: MATLAB/SIMULINK
Fig. 1 Block diagram of
SRM speed control.
Figure 2. Output flux.
Figure 3. Output current.
Figure 4. Output torque.
Figure 5. Speed.
Figure 6. Output flux.
Figure 7. Output current.
Figure 8. Output torque
Figure 9. Speed.
Figure 10. Output flux.
Figure 11. Output current.
Figure 12. Output torque.
Figure 13. Speed.
CONCLUSION:
Thus the SRM dynamic performance is
forecasted and by using MATLAB/simulink the model is simulated. SRM has been
designed and implemented for its speed control by using P, PI controller and AI
based fuzzy logic controller. We can conclude from the simulation results that
when compared with P & PI controller, the fuzzy Logic Controller meet the
required output. This paper presents a fuzzy logic controller to ensure excellent
reference tracking of switched reluctance motor drives. The fuzzy logic
controller gives a perfect speed tracking without overshoot and enchances the
speed regulation. The SRM response when controlled by FLC is more advantaged
than the conventional P& PI controller.
REFERENCES:
1. Susitra D, Jebaseeli
EAE, Paramasivam S. Switched reluctance generator - modeling, design,
simulation, analysis and control -a comprehensive review. Int J Comput Appl. 2010;
1(210):975–8887.
2. Susitra D., Paramasivam
S. Non-linear flux linkage modeling of switched reluctance machine using MVNLR
and ANFIS. Journal of Intelligent and Fuzzy Systems. 2014; 26(2):759–768.
3. Susitra D, Paramasivam
S. Rotor position estimation for
a switched reluctance
machine from phase flux linkage.
IOSR–JEEE. 2012 Nov–Dec;
3(2):7.
4. Susitra D, Paramasivam
S. Non-linear inductance modelling of switched reluctance machine using
multivariate non- linear regression technique and adaptive neuro fuzzy inference
system. CiiT International Journal of Artificial Intelligent Systems and
Machine Learning. 2011 Jun; 3(6).
5. Ramya A, Dhivya G,
Bharathi PD, Dhyaneshwaran R, Ramakrishnan P. Comparative study of speed
control of 8/6 switched reluctance motor using pi and fuzzy logic controller. IJRTE;
2012.
