Speed Controller of Switched Reluctance Motor IEEE Electrical Projects

ABSTRACT

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

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM

Block diagram of SRM speed control

Figure 1. Block diagram of SRM speed control

 

 SIMULATION MODELS

Simulation model using P controller

Figure 2. Simulation model using P controller

Simulation model using PI controller.

Figure 3. Simulation model using PI controller.

Simulink model using FLC.

Figure 4. Simulink model using FLC.

SIMULATION RESULTS

Output flux.

Figure 5. Output flux.

Output current

Figure 6. Output current

Output torque

Figure 7. Output torque.

Speed

Figure 8. 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 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).
  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

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