This paper presents the modeling and simulation of an adaptive neuro-fuzzy inference strategy (ANFIS) to control the speed of the switched Reluctance motor .The SRM control is thus a difficult to be in use in the nonlinear applications, particularly in the control of speed in automobiles. The Neuro-fuzzy system incorporates the advantages of both neural-network and fuzzy system. This controller is great additional effectual than Fuzzy logic and neural network based controller, while it has the ability of self-learning the gain values and acclimatizes accordingly to situations, thus accumulating more flexibility to the controller. A complete simulation, well-designed to the nonlinear model of Switched Reluctance Drive was premeditated using MATLAB /SIMULINK.
- SR Drive
Fig.1.Block Diagram of ANFIS Controller for SRM Plant
EXPECTED SIMULATION RESULTS:
Fig.2: Response of the speed control of SRM using FUZZY, ANN and ANFIS with speed Command 3000 RPM under no load conditions.
Fig.3: Response of the Speed and Torque Control of SRM using ANFIS with Speed Command 3000 Rpm under no load conditions.
Fig.4: Response of The Speed and Torque Control of SRM using Fuzzy, ANN and ANFIS with Speed command 4000 rpm.
Fig.5: Response of the Speed and Torque Control of SRM using ANFIS with Speed Command 4000 rpm.
Fig.6: Response of the speed control of SRM using FUZZY, ANN and ANFIS with speed Command 3000 RPM under load Conditions
Fig.7: Response of the speed and torque control of SRM using ANFIS with speed
In this paper, ANFIS-based controller was presented for SR drives. The speed and torque control method existing in this paper and comparing with the previous control schemes(fuzzy &ANN), while it can be used in both no load and load operating speeds and conditions including speed and torque transients, zero-speed standstill, and startup, and does not suppose the linear characteristics of the SR motor. Moreover, the proposed technique does not need of complex calculations to be carried out during the real-time operation, and no complex mathematical model of the SR motor is required. A main thought in the research was the robustness and reliability of the speed controlling method.
 J. P. Lyons, S. R. MacMinn, and M. A. Preston, “Flux/current methods for SRM rotor position estimation,” in Proc. IEEE Industry Application Soc. Annu. Meeting, vol. 1, 1991, pp. 482–487.
S. R. MacMinn, C. M. Steplins, and P. M. Szaresny, “Switched reluctance motor drive system and laundering apparatus employing same,” U.S. Patent 4 959 596, 1989.
 M. Ehsani, I. Husain, S. Mahajan, and K. R. Ramani, “New modulation encoding techniques for indirect rotor position sensing in switched reluctance motors,” IEEE Trans. Ind. Applicat., vol. 30, pp. 85–91, Jan./Feb. 1994.
 G. R. Dunlop and J. D. Marvelly, “Evaluation of a self commuted switched reluctance motor,” in Proc. Electric Energy Conf., 1987, pp. 317– 320.
Ramesh.Palakeerthi,Subbaiah.P ,2014, ‘High Speed Charging and Discharging Current Controller Circuit to Reduce Back EMF by NeuroFuzzy Logic ‘, International Journal of Applied Engineering Research,vol. 9, no.22