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Neural network-based adaptive global sliding mode MPPT controller design for stand-alone photovoltaic systems

Haq, Izhar Ul; Khan, Qudrat; Ullah, Safeer; Ahmed Khan, Shahid; Akmeliawati, Rini; Khan, Mehmood Ashraf; Iqbal, Jamshed


Izhar Ul Haq

Qudrat Khan

Safeer Ullah

Shahid Ahmed Khan

Rini Akmeliawati

Mehmood Ashraf Khan


The increasing energy demand and the target to reduce environmental pollution make it essential to use efficient and environment-friendly renewable energy systems. One of these systems is the Photovoltaic (PV) system which generates energy subject to variation in environmental conditions such as temperature and solar radiations. In the presence of these variations, it is necessary to extract the maximum power via the maximum power point tracking (MPPT) controller. This paper presents a nonlinear generalized global sliding mode controller (GGSMC) to harvest maximum power from a PV array using a DC-DC buck-boost converter. A feed-forward neural network (FFNN) is used to provide a reference voltage. A GGSMC is designed to track the FFNN generated reference subject to varying temperature and sunlight. The proposed control strategy, along with a modified sliding mode control, eliminates the reaching phase so that the sliding mode exists throughout the time. The system response observes no chattering and harmonic distortions. Finally, the simulation results using MATLAB/Simulink environment demonstrate the effectiveness, accuracy, and rapid tracking of the proposed control strategy. The results are compared with standard results of the nonlinear backstepping controller under abrupt changes in environmental conditions for further validation.


Haq, I. U., Khan, Q., Ullah, S., Ahmed Khan, S., Akmeliawati, R., Khan, M. A., & Iqbal, J. (2022). Neural network-based adaptive global sliding mode MPPT controller design for stand-alone photovoltaic systems. PLoS ONE, 17(1), Article e0260480.

Journal Article Type Article
Acceptance Date Nov 10, 2021
Online Publication Date Jan 20, 2022
Publication Date Jan 20, 2022
Deposit Date Nov 15, 2021
Publicly Available Date Oct 27, 2022
Journal PLoS ONE
Print ISSN 1932-6203
Electronic ISSN 1932-6203
Publisher Public Library of Science
Peer Reviewed Peer Reviewed
Volume 17
Issue 1
Article Number e0260480
Public URL


Published article (4.5 Mb)

Copyright Statement
Copyright: © 2022 Haq et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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