Investigation on ANFIS-GA controller for speed control of a BLDC fed hybrid source electric vehicle
DOI:
https://doi.org/10.4108/ew.4965Keywords:
PV, Battery Pack, ANFIS, GA, H6, VSI, BLDC MotorAbstract
The BLDC (Brushless DC Motor) is utilized in electric vehicles, space missions, and mechanical applications. Neural Network Inference System reduces torque ripple for hybrid electric vehicle (PV-Battery) along with BLDC drive to achieve efficient speed performance and stability. A hybrid input source methodology is thus put forwarded to drive the stator currents giving exactly the expressed electromagnetic torque and counter-EMF harmonics. The torque and speed control technique are directed to neural network interference system, and H6 Voltage Source Inverter (H6 VSI) drives BLDC with a gate pulse signal. We examine how an ANFIS-GA torque controller may eliminate BLDC torque ripples under uninterrupted hybrid power supply in this work. MATLAB (Simulink) results show that Genetic Algorithm (GA) improves training of ANFIS better with varying set speed conditions. The ANFIS-GA controller outperforms challenging controllers under various BLDC motor driving load conditions, proving its efficiency.
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Geetha A, Subramani C. A. Comprehensive review on energy management strategies of hybrid energy storage system for electric vehicles. Int. J. Energy Res. 2017; 41(13):1817-1834. DOI: https://doi.org/10.1002/er.3730
Aloo LA, Kihato PK, Kamau SI, Orenge RS. Interleaved boost converter voltage regulation using hybrid ANFIS-PID controller for off-grid microgrid. BEEI. 2023; 12(4):2005-2016. DOI: https://doi.org/10.11591/beei.v12i4.4906
Khan H, Khatoon S, Gaur P, Khan SA. Speed control comparison of wheeled mobile robot by ANFIS, Fuzzy and PID controllers. Int. J. Inf. Technol. 2022; 14(4):1893-1899. DOI: https://doi.org/10.1007/s41870-022-00862-8
Prajapati A, Tiwari P. Design and Simulation of ANFIS based Brushless DC Motor Control. Samriddhi - j. phys. sci. eng. technol. 2021; 13(02):70-76. DOI: https://doi.org/10.18090/samriddhi.v13i02.2
Anbazhagan G, Jayakumar S, Muthusamy S, Sundararajan SCM, Panchal H, Sadasivuni KK. An effective energy management strategy in hybrid electric vehicles using Taguchi based approach for improved performance. Energy Sources A: Recovery Util. Environ. Eff. 2022; 44(2):3418-3435. DOI: https://doi.org/10.1080/15567036.2022.2025956
Iqubal M, Stonier AA, Vanaja DS, Peter G. Design of a modular converter in hybrid EV charging station with efficient energy management system. Electr. Eng. 2023; 1(3):1-20. DOI: https://doi.org/10.1007/s00202-023-01822-6
Abdelfattah H, Mosaad MI, Ibrahim NF. Adaptive neuro fuzzy technique for speed control of six-step brushless DC motor. Indones. J. Electr. Eng. Inform. 2021; 9(2): 302-312. DOI: https://doi.org/10.52549/ijeei.v9i2.2614
Subramani S, Vijayarangan KK, Chenniappan M. Improved African Buffalo Optimization-Based Takagi–Sugeno–Kang Fuzzy PI Controller for Speed Control in BLDC Motor. Electr. Power Compon. Syst. 2023; 1(2): 1-15.
Gharajeh MS, Jond HB. An intelligent approach for autonomous mobile robot’s path planning based on adaptive neuro-fuzzy inference system. Ain Shams Eng. J. 2022; 13(1):101-491. DOI: https://doi.org/10.1016/j.asej.2021.05.005
Dasari M, Reddy AS, Kumar MV. A comparative analysis of converters performance using various control techniques to minimize the torque ripple in BLDC drive system. SUSCOM. 2022; 33(1):100-648. DOI: https://doi.org/10.1016/j.suscom.2021.100648
Jegajothi B, Geethamahalakshmi G, Raja A, Mahendran N. An efficient metaheuristic optimization based fuzzy controller for brushless DC drives lifetime expansion. Mater. Today: Proc. 2022; 56(5):3343-3348. DOI: https://doi.org/10.1016/j.matpr.2021.10.176
Okoji AI, Anozie AN, Omoleye JA. Evaluating the thermodynamic efficiency of the cement grate clinker cooler process using artificial neural networks and ANFIS. Ain Shams Eng. J. 2022; 13(5):101-704. DOI: https://doi.org/10.1016/j.asej.2022.101704
Gayatri Sarman KVSH, Madhu T, Mallikharjuna Prasad A. Fault diagnosis of BLDC drive using advanced adaptive network-based fuzzy inference system. Soft Comput. 2021; 25(20):12759-12774. DOI: https://doi.org/10.1007/s00500-021-06046-z
Bharanigha V, Shuaib YM. Minimization of torque ripples with optimized controller based four quadrant operation & control of BLDC motor. Adv. Eng. Softw. 2022; 172(1):103-192. DOI: https://doi.org/10.1016/j.advengsoft.2022.103192
Kannan R, Sundharajan V. A novel MPPT controller based PEMFC system for electric vehicle applications with interleaved SEPIC converter. Int. J. Hydrog. Energy. 2023; 48(38):14391-14405. DOI: https://doi.org/10.1016/j.ijhydene.2022.12.284
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