Skip to main content

Maximum power point tracking technique based on variable step size with sliding mode controller in photovoltaic system


Due to the economic and technical advantages, the use of solar energy is expanding in developed countries. The extraction of maximum power in solar power plants is an important issue that requires extensive research. Extracting the maximum possible power in solar power plants can increase the efficiency of this type of renewable energy sources (RESs). Climatic condition is a very important feature of solar systems. In fact, radiation and temperature are two important parameters that affect the efficiency of solar systems. This paper suggests a novel maximum power point tracking (MPPT) technique based on the sliding mode controller (SMC) to extract the maximum power of photovoltaic (PV) systems in different climatic circumstances. To obtain the optimal coefficients of the SMC online, the Grey wolf optimizer (GWO) algorithm is employed. SMC coefficients are applied for the variable perturb and observe (P&O) step of MPPT. The proposed GWO-SMC controller can eliminate oscillations in the transient mode and guarantee stability. The findings of the simulation indicate that with the use of an MPPT controller for the solar-PV system, such as P&O, Fuzzy Logic (FLC), Incremental Conductance (INC), the β method, and hill climbing (HC) MPPT, the system will operate more efficiently. The method that has been suggested is tested in a number of different climate conditions. The findings indicate that the proposed technique has an efficiency of 99%, which demonstrates a substantially superior response time when reaching the MPP in comparison to prevalent methods, which have an efficiency of 92 to 97%. The results of the simulations allow for the various approaches to be ranked as follows: 1. GWO-SMC, 2. FLC, 3. INC, 4. β method, 5. P&O, 6. HC with response times of 0.14 s, 0.17 s, 0.23 s, 0.25, 0.28 s and 0.35, respectively. The fluctuations using the combinatorial GWO-SMC technique is 4.31 W, while that of the P&O is 74.56 W. Through simulation and testing with the MATLAB software, the developed method's performance is evaluated to make a comparison.

This is a preview of subscription content, access via your institution.

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

Data availability

Enquiries about data availability should be directed to the authors.



Maximum power point tracking


Sliding mode controller




Grey wolf optimizer


Perturb and observe


Renewable energy sources


Incremental conductance




Fuzzy logic controller


Artificial neural networks


Genetic algorithm


Partial shading conditions


Pulse-width modulation

T :


G :


n :

Ideality factor

Eg :



Phase lock loop


  • Cortajarena JA, Barambones O, Alkorta P, De Marcos J (2017) Sliding mode control of grid-tied single-phase inverter in a photovoltaic MPPT application. Sol Energy 1(155):793–804

    Article  Google Scholar 

  • Dadfar S, Wakil K, Khaksar M, Rezvani A, Miveh MR, Gandomkar M (2019) Enhanced control strategies for a hybrid battery/photovoltaic system using FGS-PID in grid-connected mode. Int J Hydrogen Energy 44(29):14642–14660

    Article  Google Scholar 

  • Delavari H, Zolfi M (2021) Maximum power point tracking in photovoltaic systems using indirect adaptive fuzzy robust controller. Soft Comput 15:1–7

    MATH  Google Scholar 

  • Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18

    Article  Google Scholar 

  • El Khazane J, Tissir EH (2018) Achievement of MPPT by finite time convergence sliding mode control for photovoltaic pumping system. Sol Energy 15(166):13–20

    Article  Google Scholar 

  • Fallahpour A, Nayeri S, Sheikhalishahi M, Wong KY, Tian G, Fathollahi-Fard AM (2021) A hyper-hybrid fuzzy decision-making framework for the sustainable-resilient supplier selection problem: a case study of Malaysian palm oil industry. Environ Sci Pollut Res 28:1–21

    Google Scholar 

  • Fathollahi-Fard AM, Ahmadi A, Karimi B (2021) Multi-objective optimization of home healthcare with working-time balancing and care continuity. Sustainability 13(22):12431

    Article  Google Scholar 

  • Fathollahi-Fard AM, Niaz Azari M, Hajiaghaei-Keshteli M (2021) An improved red deer algorithm for addressing a direct current brushless motor design problem. Scientia Iranica 28(3):1750–1764

    Google Scholar 

  • Ghadami N, Gheibi M, Kian Z, Faramarz MG, Naghedi R, Eftekhari M, Fathollahi-Fard AM, Dulebenets MA, Tian G (2021) Implementation of solar energy in smart cities using an integration of artificial neural network, photovoltaic system and classical Delphi methods. Sustain Cities Soc 1(74):103149

    Article  Google Scholar 

  • Gheibi M, Eftekhari M, Akrami M, Emrani N, Hajiaghaei-Keshteli M, Fathollahi-Fard AM, Yazdani M (2022) A sustainable decision support system for drinking water systems: resiliency improvement against cyanide contamination. Infrastructures 7(7):88

    Article  Google Scholar 

  • Gholizadeh H, Fathollahi-Fard AM, Fazlollahtabar H, Charles V (2022) Fuzzy data-driven scenario-based robust data envelopment analysis for prediction and optimisation of an electrical discharge machine’s parameters. Expert Syst Appl 1(193):116419

    Article  Google Scholar 

  • González-Castaño C, Restrepo C, Kouro S, Rodriguez J (2021) MPPT algorithm based on artificial bee colony for PV system. IEEE Access 17(9):43121–43133

    Article  Google Scholar 

  • Ishaque K, Salam Z, Amjad M, Mekhilef S (2012) An improved particle swarm optimization (PSO)–based MPPT for PV with reduced steady-state oscillation. IEEE Trans Power Electron 27(8):3627–3638

    Article  Google Scholar 

  • Javed S, Ishaque K (2022) A comprehensive analyses with new findings of different PSO variants for MPPT problem under partial shading. Ain Shams Eng J 13(5):101680

    Article  Google Scholar 

  • Ji W, Qiu J, Karimi HR (2019) Fuzzy-model-based output feedback sliding mode control for discrete-time uncertain nonlinear systems. IEEE Trans Fuzzy Syst 28(8):1519–1530

    Article  Google Scholar 

  • Khan MJ, Mathew L (2021) Artificial neural network-based maximum power point tracking controller for real-time hybrid renewable energy system. Soft Comput 25(8):6557–6575

    Article  Google Scholar 

  • Li Y, Samad S, Ahmed FW, Abdulkareem SS, Hao S, Rezvani A (2020) Analysis and enhancement of PV efficiency with hybrid MSFLA–FLC MPPT method under different environmental conditions. J Clean Prod 20(271):122195

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  • Mohanty S, Subudhi B, Ray PK (2015) A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions. IEEE Trans Sustain Energy 7(1):181–188

    Article  Google Scholar 

  • Pasha J, Nwodu AL, Fathollahi-Fard AM, Tian G, Li Z, Wang H, Dulebenets MA (2022) Exact and metaheuristic algorithms for the vehicle routing problem with a factory-in-a-box in multi-objective settings. Adv Eng Inform 1(52):101623

    Article  Google Scholar 

  • Perruquetti W, Barbot JP (2002) Sliding mode control in engineering. Marcel Dekker, New York

    Book  Google Scholar 

  • Pouresmaeil H, Faramarz MG, ZamaniKherad M, Gheibi M, Fathollahi-Fard AM, Behzadian K, Tian G (2022) A decision support system for coagulation and flocculation processes using the adaptive neuro-fuzzy inference system. Int J Environ Sci Technol 22:1–2

    Google Scholar 

  • Rezvani A, Izadbakhsh M, Gandomkar M (2016) Microgrid dynamic responses enhancement using artificial neural network-genetic algorithm for photovoltaic system and fuzzy controller for high wind speeds. Int J Numer Model Electron Netw Devices Fields 29(2):309–332

    Article  Google Scholar 

  • Seydanlou P, Jolai F, Tavakkoli-Moghaddam R, Fathollahi-Fard AM (2022) A multi-objective optimization framework for a sustainable closed-loop supply chain network in the olive industry: hybrid meta-heuristic alg ‘orithms. Expert Syst Appl 13:117566

    Article  Google Scholar 

  • Shahsavar MM, Akrami M, Gheibi M, Kavianpour B, Fathollahi-Fard AM, Behzadian K (2021) Constructing a smart framework for supplying the biogas energy in green buildings using an integration of response surface methodology, artificial intelligence and petri net modelling. Energy Convers Manage 15(248):114794

    Article  Google Scholar 

  • Shengqing L, Fujun L, Jian Z, Wen C, Donghui Z (2020) An improved MPPT control strategy based on incremental conductance method. Soft Comput 24(8):6039–6046

    Article  Google Scholar 

  • Sheskin DJ (2003) Handbook of parametric and nonparametric statistical procedures. Chapman and Hall/CRC, London

    Book  MATH  Google Scholar 

  • Soufyane Benyoucef A, Chouder A, Kara K, Silvestre S (2015) Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions. Appl Soft Comput 32:38–48

    Article  Google Scholar 

  • Tian G, Zhang C, Fathollahi-Fard AM, Li Z, Zhang C, Jiang Z (2022) An enhanced social engineering optimizer for solving an energy-efficient disassembly line balancing problem based on bucket brigades and cloud theory. IEEE Trans Ind Inform.

    Article  Google Scholar 

  • Tian G, Fathollahi-Fard AM, Ren Y, Li Z, Jiang X (2022) Multi-objective scheduling of priority-based rescue vehicles to extinguish forest fires using a multi-objective discrete gravitational search algorithm. Inf Sci 1(608):578–596

    Article  Google Scholar 

  • Torres JZ, Cieslak J, Henry D, Davila J (2019) A sliding mode control in a backstepping setup for rendezvous mission on a circular orbit

  • Wu D, Nariman GS, Mohammed SQ, Shao Z, Rezvani A, Mohajeryami S (2019) Modeling and simulation of novel dynamic control strategy for PV–wind hybrid power system using FGS−PID and RBFNSM methods. Soft Comput 10:1–23

    Google Scholar 

  • Yu H, Dai H, Tian G, Wu B, Xie Y, Zhu Y, Zhang T, Fathollahi-Fard AM, He Q, Tang H (2021) Key technology and application analysis of quick coding for recovery of retired energy vehicle battery. Renew Sustain Energy Rev 1(135):110129

    Article  Google Scholar 

Download references


This work was supported by the National Natural Science Foundation of China (No.61862051), the Science and Technology Foundation of Guizhou Province (No.ZK[2022]549, No. [2019]1299), the Top-notch Talent Program of Guizhou province (No.KY[2018]080), the Natural Science Foundation of Education of Guizhou province(No.[2019]203) and the Funds of Qiannan Normal University for Nationalities (No. qnsy2018003, No. qnsy2019rc09, No. qnsy2018JS013, No. qnsyrc201715).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Jasni Mohamad Zain.

Ethics declarations

Conflict of interest

Authors declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Appendix A

GWO parameters:

Number of search-agents = 100 and maximum number of iteration = 50.

Appendix B

See Table 8.

Table 8 PI coefficients

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hai, T., Zain, J.M. & Nakamura, H. Maximum power point tracking technique based on variable step size with sliding mode controller in photovoltaic system. Soft Comput 27, 3829–3845 (2023).

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Photovoltaic (PV)
  • Maximum power point (MPP)
  • Stability
  • Dynamic
  • Grey wolf optimizer (GWO) algorithm