Artificial Intelligence-Based Controller for DC-DC Flyback Converter
Source abstract
Abstract
This paper presents an intelligent voltage controller designed on the basis of an adaptive neuro-fuzzy inference system (ANFIS) for a flyback converter (FC) working in continuous conduction mode (CCM). The union of fuzzy logic (FL) and adaptive neural networks (ANN) makes ANFIS more robust against model parameters’ uncertainties and perturbations in input voltage or load current. ANFIS inherits the advantages of structured knowledge representation from FL and learning capability from NN. Comparative analysis showed that the ANFIS controller offers not only the superior transient response characteristics, but also excellent steady-state characteristics compared to those of the FL controller (FLC) and proportional–integral–derivative (PID) controllers, thus validating its superiority over these traditional controllers. For this purpose, MATLAB/Simulink environment-based simulation results are presented for validation of the proposed converter compensated system under all operating conditions.
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Research context
OpenAlex topics: Advanced DC-DC Converters · Multilevel Inverters and Converters · Microgrid Control and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Electrical Engineering Department, The University of Lahore, Lahore 54000, Pakistan
College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia
Electrical and Computer Engineering Department, Dalhousie University, Halifax, NS B3H 4R2, Canada
Electrical Engineering Department, University of the Punjab, Lahore 54590, Pakistan
Automatics and Applied Software Department, “Aurel Vlaicu” University of Arad, Arad 310130, Romania
Department of Electrical Engineering, Islamic University of Madinah, Madinah 41411, Saudi Arabia
Department of Electrical Engineering, College of Engineering, Majmaah University, Al-Majmaah 11952, Saudi Arabia
Funding
The Deanship of Scientific Research at King Khalid University for funding this work through Research Group Project under grant number R.G.P. 1/166/40.
References (49)
Show source-provided references
1. Sarif, M.S.M., Pei, T.X., and Annuar, A.Z. (2018, January 28–29). Modeling, design and control of bidirectional DC-DC converter using state-space average model. Proceedings of the IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), Penang, Malaysia.10.1109/ISCAIE.2018.8405509 ↗
2. Investigation of a Voltage-Mode Controller for a dc-dc Multilevel Boost Converter
3. MShahid, A., Yasin, A.R., and Ahmad, S. (2016, January 5–6). Practical sliding mode controller in Dc-Dc converters for use in hybrid vehicles. Proceedings of the 19th International Multi-Topic Conference (INMIC), Islamabad, Pakistan.10.1109/INMIC.2016.7840128 ↗
4. A Novel ZCS-PWM Flyback Converter with a Simple ZCS-PWM Commutation Cell10.1109/TIE.2007.911917 ↗
5. Iqbal, H.K., and Abbas, G. (2014, January 8–10). Design and analysis of SMC for second order DC-DC flyback converter. Proceedings of the 17th IEEE International Multi Topic Conference 2014, Karachi, Pakistan.10.1109/INMIC.2014.7097398 ↗
6. A Three-Phase Unity Power Factor Single-Stage AC–DC Converter Based on an Interleaved Flyback Topology10.1109/TPEL.2010.2060359 ↗
7. Ohsato, T., Satoh, N., and Sekiya, H. (2017, January 3–7). A flyback converter using power-MOSFETs to achieve high-frequency operation beyond 10MHz. Proceedings of the IEEE 3rd International Future Energy Electronics Conference and ECCE Asia, Kaohsiung, Taiwan.10.1109/IFEEC.2017.7992195 ↗
8. Study on the Single-Stage Forward-Flyback PFC Converter With QR Control10.1109/TPEL.2015.2404859 ↗
9. Isolated step-up converter based on flyback converter and charge pumps10.1049/iet-pel.2013.0825 ↗
10. High-Efficiency Isolated Boost DC–DC Converter for High-Power Low-Voltage Fuel-Cell Applications10.1109/TIE.2009.2036024 ↗
11. Yousefzadeh, V., Shirazi, M., and Maksimovic, D. (March, January 25). Minimum Phase Response in Digitally Controlled Boost and Flyback Converters. Proceedings of the APEC—Twenty-Second Annual IEEE Applied Power Electronics Conference and Exposition, Anaheim, CA, USA.
12. Overview of control Techniques for FC-FC converter
13. A Fast-Response Sliding-Mode Controller for Boost-Type Converters with a Wide Range of Operating Conditions10.1109/TIE.2007.905969 ↗
14. Salimi, M., Soltani, J., Zakipour, A., and Hajbani, V. (2013, January 13–14). Sliding mode control of the DC-DC flyback converter with zero steady-state error. Proceedings of the 4th Annual International Power Electronics, Drive Systems and Technologies Conference, Tehran, Iran.10.1109/PEDSTC.2013.6506695 ↗
15. Peak current-controlled CRM flyback PFC converter with square of line voltage-compensated primary current envelope10.1049/el.2014.4420 ↗
16. Chandranadhan, V.R., and Renjini, G. (2015, January 19–20). Average current mode control of improved bridgeless flyback rectifier with bidirectional switch. Proceedings of the International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015], Nagercoil, India.10.1109/ICCPCT.2015.7159416 ↗
17. Quasi-Resonant (QR) Controller with Adaptive Switching Frequency Reduction Scheme for Flyback Converter10.1109/TIE.2016.2523931 ↗
18. Chen, K., and Liang, T. (2014, January 18–21). Design of Quasi-resonant flyback converter control IC with DCM and CCM operation. Proceedings of the International Power Electronics Conference, Hiroshima, Japan.
19. Designing and Tuning of PI Controller for Flyback Converter10.14445/22315381/IJETT-V13P225 ↗
20. Design of Novel Fly-Back Converter Using PID Controller
21. Design, Modelling and Simulation of a PID Controller for Buck Boost and Cuk Converter
22. Yilmaz, U., Kircay, A., and Borekci, S. (2017, January 19–21). PV system flyback converter-controlled PI control to charge battery under variable temperature and irradiance. Proceedings of the Electronics, Palanga, Lithuania.10.1109/ELECTRONICS.2017.7995223 ↗
23. Dynamic modelling and controller design of flyback converter10.1109/7.805441 ↗
24. Garcia-Rodriguez, L.A., Williams, E., Balda, J.C., Gonzalez-Llorente, J., and Chiacchiarini, H. (2015, January 22–25). Control of a flyback converter operating in BCM using the natural switching surface. Proceedings of the IEEE 6th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), Aachen, Germany.10.1109/PEDG.2015.7223042 ↗
25. Chen, X., Jiang, T., Zhao, S., Zeng, H., and Zhang, J. (2013, January 17–21). Evaluation of primary side control schemes for flyback converter with constant current output. Proceedings of the Twenty-Eighth Annual IEEE Applied Power Electronics Conference and Exposition (APEC), Long Beach, CA, USA.10.1109/APEC.2013.6520548 ↗
26. Singh, A., and Londhe, P.S. (2017, January 26–27). Design of signed distance method based fuzzy logic controller for TITO process. Proceedings of the Recent Developments in Control, Automation & Power Engineering (RDCAPE), Noida, India.10.1109/RDCAPE.2017.8358231 ↗
27. and Kuo, C. (2013, January 6–8). Development of a fuzzy logic wall following controller for steering mobile robots. Proceedings of the International Conference on Fuzzy Theory and Its Applications (iFUZZY), Taipei, Taiwan.
28. Deepa, K., Jeyanthi, R., Mohan, S., and Kumar, M.V. (2014, January 9–11). Fuzzy based flyback converter. Proceedings of the International Conference on Advances in Electrical Engineering (ICAEE), Vellore, India.10.1109/ICAEE.2014.6838477 ↗
29. Subbarao, M., Babu, C.S., Satyanarayana, S., and Kumar, S.L.V.S. (2014, January 19–20). Fuzzy controlled single stage AC/DC converter with PFC to drive LEDs. Proceedings of the International Conference on Smart Electric Grid (ISEG), Guntur, India.10.1109/ISEG.2014.7005585 ↗
30. Comparative Study of PID Based VMC and Fuzzy Logic Controllers for Flyback Converter
31. Hamdan, H., and Garibaldi, J.M. (2010, January 18–23). Adaptive neuro-fuzzy inference system (ANFIS) in modelling breast cancer survival. Proceedings of the International Conference on Fuzzy Systems, Barcelona, Spain.10.1109/FUZZY.2010.5583997 ↗
32. Evaluation of a new neutron energy spectrum unfolding code based on an Adaptive Neuro-Fuzzy Inference System (ANFIS)10.1093/jrr/rrx087 ↗
33. Dynamic Behavioral Modeling of Power Amplifiers Using ANFIS-Based Hammerstein10.1109/LMWC.2008.2003483 ↗
34. Farayola, A.M., Hasan, A.N., and Ali, A. (2017, January 21–23). Curve fitting polynomial technique compared to ANFIS technique for maximum power point tracking. Proceedings of the 8th International Renewable Energy Congress (IREC), Amman, Jordan.10.1109/IREC.2017.7926047 ↗
35. Mohammed, S.S., Devaraj, D., and Ahamed, T.P.I. (2016, January 21–23). Maximum power point tracking system for standalone solar PV power system using Adaptive Neuro-Fuzzy Inference System. Proceedings of the Biennial International Conference on Power and Energy Systems: Towards Sustainable Energy (PESTSE), Bangalore, India.
36. Chu, Y., Yuan, L., and Chiang, H. (2015, January 25–28). ANFIS-based maximum power point tracking control of PV modules with DC-DC converters. Proceedings of the 18th International Conference on Electrical Machines and Systems (ICEMS), Pattaya, Thailand.10.1109/ICEMS.2015.7385123 ↗
37. Tarek, B., Said, D., and Benbouzid, M.E.H. (2013, January 27–30). Maximum Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro-Fuzzy ANFIS. Proceedings of the Eighth International Conference and Exhibition on Ecological Vehicles and Renewable Energies (EVER), Monte Carlo, Monaco.10.1109/EVER.2013.6521559 ↗
38. Simulation and Practical Implementation of ANFIS-Based MPPT Method for PV Applications Using Isolated Ćuk Converter10.1155/2017/3106734 ↗
39. Al-Nussairi, M.K., Bayindir, R., and Hossain, E. (2017, January 5–8). Fuzzy logic controller for Dc-Dc buck converter with constant power load. Proceedings of the IEEE 6th International Conference on Renewable Energy Research and Applications (ICRERA), San Diego, CA, USA.10.1109/ICRERA.2017.8191239 ↗
40. A Survey on Analysis and Design of Model-Based Fuzzy Control Systems10.1109/TFUZZ.2006.883415 ↗
41. Swathy, M.K., Jantre, M.S., Jadhav, M.Y., Labde, M.S.M., and Kadam, M.P. (2018, January 29–31). Design and Hardware Implementation of Closed Loop Buck Converter Using Fuzzy Logic Controller. Proceedings of the Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India.10.1109/ICECA.2018.8474570 ↗
42. Bendaoud, K., Krit, S., Kabrane, M., Ouadani, H., Elaskri, M., Karimi, K., Elbousy, H., and Elmaimouni, L. (2017, January 8–10). Fuzzy logic controller (FLC): Application to control DC-DC buck converter. Proceedings of the International Conference on Engineering & MIS (ICEMIS), Monastir, Tunisia.10.1109/ICEMIS.2017.8272980 ↗
43. de Martins, J.K.E.C., and de Araújo, F.M.U. (2015, January 21–23). Nonlinear system identification based on modified ANFIS. Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Colmar, France.
44. ANFIS-Based Control of a Grid-Connected Hybrid System Integrating Renewable Energies, Hydrogen and Batteries10.1109/TII.2013.2290069 ↗
45. Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning10.1109/TLT.2011.36 ↗
46. Hiremath, S., and Patra, S.K. (August, January 29). Transmission rate prediction for Cognitive Radio using Adaptive Neural Fuzzy Inference System. Proceedings of the 5th International Conference on Industrial and Information Systems, Mangalore, India.
47. ANFIS-based sensor fault-tolerant control for hybrid grid10.1049/iet-gtd.2016.1465 ↗
48. Using adaptive network based fuzzy inference system to forecast regional electricity loads10.1016/j.enconman.2007.06.015 ↗
49. Abbas, G., Muazzam, H., Farooq, U., Gu, J., and Asad, M.U. (2015). Comparative Analysis of Analog Controllers for DC-DC Buck Converter. J. Autom. Control Eng., 3.10.12720/joace.3.6.447-451 ↗