Fuzzy logic control has become an important methodology in control engineering. The paper suggest a Fuzzy Logic Controller (FLC) for controlling a speed of SRM drive. The impartial 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 work of the effective controller. The present work focus 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 work and high robustness than a conventional P & PI controller. Simulation is carried out using Matlab/Simulink.
KEYWORDS: P Controller, PI Controller, Fuzzy Logic Controller, Switched Reluctance Motor
Figure 1. Block diagram of SRM speed control
Figure 2. Simulation model using P controller
Figure 3. Simulation model using PI controller.
Figure 4. Simulink model using FLC.
Figure 5. Output flux.
Figure 6. Output current
Figure 7. Output torque.
Figure 8. Speed.
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.
- 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.
- 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.
- Susitra D, Paramasivam S. Rotor position estimation for a switched reluctance machine from phase flux linkage. IOSR–JEEE. 2012 Nov–Dec; 3(2):7.
- Susitra D, Paramasivam S. Non-linear inductance modeling 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).
- 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