Skip to main content
SpringerLink
Log in

Fuzzy logic control on FPGA for two axes solar tracking

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper presents a practical fuzzy controller two axes solar tracking-based realization on digital FPGA hardware. The fuzzy logic control is based according to Mamdani rules, alpha levels, max–min operations and defuzzification method. Operations and algorithms are reduced using look-up tables for the membership values which are stored as digital values and accessed to the control process. The feasibility and versatility of the proposed technique as well as its potential as a low-cost design for solar tracking control on digital field-programmable gate array (FPGA) are shown by simulated and experimental results in a photovoltaic system under different operation conditions. The proposed realization exhibits good performance related to the control and efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Youssef A, El-Telbany M, Zekry A (2017) The role of artificial intelligence in photo-voltaic systems design and control: a review. Renew Sustain Energy Rev 78:72–79

    Article  Google Scholar 

  2. Antonio L, Steven H (2003) Handbook of photovoltaic science and engineering institute of energy conversion, University of Delaware, USA. Wiley Editorial, New York, pp 1020–1028

    Google Scholar 

  3. Sumathi V, Jayapragash R, Bakshi A, Akell PK (2017) Solar tracking methods to maximize PV system output: a review of the methods adopted in recent decade. Renew Sustain Energy Rev 74:130–138

    Article  Google Scholar 

  4. Chavez UE, Yuri V (2013) Investigation of solar hybrid electric/thermal system with radiation concentrator and thermoelectric generator. Int J Photoenergy 2013:1–7

    Article  Google Scholar 

  5. Njoku HO (2014) Solar photovoltaic potential in Nigeria. J Energy Eng 140:1–7

    Article  Google Scholar 

  6. Zadey S, Dutt S (2013) Design of converter for low power photovoltaic conversion system. Int J Adv Res Electr Electron Instrum Eng 2(6):2733–2739

    Google Scholar 

  7. Aldali Y, Celik AN, Muneer T (2013) Modeling and experimental verification of solar radiation on a sloped surface, photovoltaic cell temperature, and photovoltaic efficiency. J Energy Eng 139:8–11

    Article  Google Scholar 

  8. Abu-Rub H, Iqbal A, Ahmed Sk M (2012) Adaptive neuro-fuzzy inference system-based maximum power point tracking of solar PV modules for fast varying solar radiations. Int J Sustain Energy 31(6):383–398

    Article  Google Scholar 

  9. Chekired F, Larbes C, Mellit A (2014) Comparative study between two intelligent MPPT-controllers implemented on FPGA: application for photovoltaic systems. Int J Sustain Energy 33(3):483–499

    Article  Google Scholar 

  10. Gad HH, Haikal AY, Ali HA (2017) New design of the PV panel control system using FPGA-based MPSoC. Sol Energy 146:243–256

    Article  Google Scholar 

  11. Wong J, Lim Y, Morris E (2015) Novel fuzzy controlled energy storage for low-voltage distribution networks with photovoltaic systems under highly cloudy conditions. J Energy Eng 141(1):1–15

    Article  Google Scholar 

  12. Ansari MF, Chatterji S, Iqbal A (2010) A fuzzy logic control scheme for a solar photovoltaic system for a maximum power point tracker. Int J Sustain Energy 29(4):245–255

    Article  Google Scholar 

  13. Altas IH, Sharaf AM (2008) A novel maximum power fuzzy logic controller for photovoltaic solar energy systems. Renew Energy 33(3):388–399

    Article  Google Scholar 

  14. Barsoum N (2009) Implementation of a prototype for a traditional solar tracking system. In: Computer modeling and simulation, 2009. EMS’09. Third UK sim european symposium, IEEE, pp 23–30

  15. Batayneh W, Owais A, Nairoukh M (2013) An intelligent fuzzy based tracking controller for a dual-axis solar PV system. Autom Constr 29:100–106

    Article  Google Scholar 

  16. Chekired F, Larbes C, Rekioua D, Haddad F (2011) Implementation of a MPPT fuzzy controller for photovoltaic systems on FPGA circuit. Energy Procedia 6:541–549

    Article  Google Scholar 

  17. Ahmad S, Shafie S, Ab Kadir MZA (2013) Power feasibility of a low power consumption solar tracker. Procedia Environ Sci 17:494–502

    Article  Google Scholar 

  18. Prasanth Ram J, Rajasekar N, Miyatake M (2017) Design and overview of maximum power point tracking techniques in wind and solar photovoltaic systems: a review. Renew Sustain Energy Rev 73:1138–1159

    Article  Google Scholar 

  19. Othman AM, El-arini MM, Ghitas A, Fathy A (2012) Real world maximum power point tracking simulation of PV system based on fuzzy logic control. NRIAG J Astron Geophys 1(2):186–194

    Article  Google Scholar 

  20. Patyra MJ, Mlynek DM (2012) Fuzzy logic: implementation and applications. Wiley Editorial, New York, pp 237–242

    Google Scholar 

  21. Ozcelik S, Prakash H, Challoo R (2011) Two-axis solar tracker analysis and control for maximum power generation. Procedia Comput Sci 6:457–462

    Article  Google Scholar 

  22. Tsai HL, Tu CS, Su YJ (2008) Development of generalized photovoltaic model using MATLAB/SIMULINK. In: Proceedings of the world congress on engineering and computer science WCECS, pp 1–6

  23. Mendieta Garrido A (2013) Design of a PV plant for the Institute of Technology and Higher Education of Ecatepec, Master Dissertation

  24. Hernández Zavala MA (2009) Arquitectura de Alto Rendimiento para Procesadores Difusos. Doctoral Dissertation, IPN, Mexico

  25. Lughofer E, Sayed-Mouchaweh M (2015) Autonomous data stream clustering implementing split-and-merge concepts: towards a plug-and-play approach. Inf Sci 304:54–79

    Article  Google Scholar 

  26. Lughofer E (2012) Hybrid active learning for reducing the annotation effort of operators in classification systems. Pattern Recognit 45:884–896

    Article  Google Scholar 

  27. de Jesus Rubio J, Ochoa G, Meda JA, Rangel VI, Pacheco J (2015) Acquisition system and analytic fuzzy model of a manufactured wind turbine. IEEE Lat Am Trans 13(12):3879–3884

    Article  Google Scholar 

  28. Rubio JJ, Bouchachia A (2016) MSAFIS: an evolving fuzzy inference system. Soft Comput. doi:10.1007/s00500-015-1946-4

    Google Scholar 

  29. Yurkovich S, Widjaja M (1996) Fuzzy controller synthesis for an inverted pendulum system. Control Eng Pract 4(4):455–469

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mechanical and Mechatronic Department, Tecnológico de Estudios Superiores de Ecatepec, Av. Tecnológico, Esq. Av. Hank González, Col. Valle de Anáhuac, 55210, Ecatepec, Estado de México, Mexico

    J. de la Cruz-Alejo, R. Antonio-Méndez & M. Salazar-Pereyra

Authors
  1. J. de la Cruz-Alejo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Antonio-Méndez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Salazar-Pereyra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Salazar-Pereyra.

Ethics declarations

Conflict of interest

The authors state that they have no conflict of interest with the publication of this research paper. Likewise, they also would like to thank the reviewers for their valuable comments and suggestions to improve the present work.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de la Cruz-Alejo, J., Antonio-Méndez, R. & Salazar-Pereyra, M. Fuzzy logic control on FPGA for two axes solar tracking. Neural Comput & Applic 31, 2469–2483 (2019). https://doi.org/10.1007/s00521-017-3207-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-017-3207-1

Keywords

Search

Navigation