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| dc.contributor.author | YALEW, MERSHA | |
| dc.date.accessioned | 2022-11-22T08:14:06Z | |
| dc.date.available | 2022-11-22T08:14:06Z | |
| dc.date.issued | 2022-08 | |
| dc.identifier.uri | http://ir.bdu.edu.et/handle/123456789/14512 | |
| dc.description.abstract | Air pollution in the world increase due to the emission of pollutant gas from vehicles. To solve this problem electric vehicle with traction motor instead of ICE is the emerging technology but speed control of traction motor is the big challenge in electric vehicle. This thesis work presents the design of a neuro-fuzzy sliding mode controller for switched reluctance motor speed control applied to electric vehicle traction. The proposed Neuro-Fuzzy sliding mode controller was trained with input and output data of the sliding surface, change in the sliding surface, equivalent part inputs, and signum function output to replace the designed integral slid-ing mode controller signum function by ANFIS and its equivalent part by neural network. The simulated integral sliding mode controller provided all of the data used to train the proposed controller. To replace the signum function of the designed integral sliding mode controller with ANFIS, the ANFIS was trained from the data of the sliding surface, change in sliding surface and signum function output using a hybrid learning algorithm, and to replace the equivalent part of the designed ISMC by a neural network, the neural network was trained from the data of equivalent part inputs (reference speed, torque load, actual speed and speed error) and the sliding surface using the Levenberg-Marquardt backpropagation algorithm. The stability of the designed integral sliding mode controller is proven using the Lyapunov stability criteria. The performance of the proposed controller was compared with ISMC and ANFIS sliding mode controllers using MATLAB\Simulink simulation results with different operating conditions. It is observed from the simulation results of the system using ISMC, ANFIS-SMC, and N-ANFIS-SMC controllers that with 2000 rpm step reference speed, the percentage overshoots were 0.725%, 0.725% and 0.54% respectively and the N-ANFIS-SMC controller reduces the torque ripple by 34.1% and 43.2% than using ANFIS-SMC and ISMC controllers respectively. This shows that the N-ANFIS-SMC has decreased overshoot and torque ripple. Also, the chat-tering effect is minimized and the speed response reaches its setpoint value quickly in the N-ANFIS-SMC controlled traction motor. This shows the effectiveness of the designed N-ANFIS-SMC controller. On the other hand, ANFIS-SMC shows better performance than ISMC for vehicle traction motor. This is because the discontinuous part of ISMC is replaced by AN-FIS to make it continuous. Key Words: ANFIS, battery, electric vehicle, neural network, sliding mode control, switched reluctance motor. | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | ELECTRICAL AND COMPUTER ENGINEERING | en_US |
| dc.title | Design Neuro-Fuzzy sliding mode controller for switched reluctance motor speed control applicable to electric vehicle | en_US |
| dc.type | Thesis | en_US |
