Adaptive ANN-Based MPPT Control for Piezoelectric Vibration Energy Harvesting System
Ismail Alazhari Abubaker Bashar Omer
Ismail Alazhari Abubaker Bashar Omer, Renewable Energy, University of Blue Nile, Ad-Damazin, Sudan.
Manuscript received on 28 December 2025 | First Revised Manuscript received on 21 January 2026 | Second Revised Manuscript received on 20 February 2026 | Manuscript Accepted on 15 March 2026 | Manuscript published on 30 March 2026 | PP: 1-8 | Volume-13 Issue-3, March 2026 | Retrieval Number: 100.1/ijies.C473715030226 | DOI: 10.35940/ijies.C4737.13030326
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Piezoelectric energy harvesting systems often suffer from suboptimal power extraction due to the time-varying nature of mechanical vibrations and the nonlinear impedance characteristics of piezoelectric materials. We propose a real-time artificial neural network (ANN)-based maximum power point tracking (MPPT) controller to dynamically optimize the power transfer from a piezoelectric source to a load. The ANN directly maps the instantaneous piezoelectric voltage to the optimal duty cycle of a buck converter. The proposed method employs a single hidden layer with 10 nodes, ensuring computational efficiency while capturing the nonlinear relationship between the input voltage and the optimal duty cycle. The system integrates a full wave rectifier to convert the alternating-current output of the piezoelectric bender into a direct-current voltage, which the ANN then processes to generate the control signal for the pulse-width modulation (PWM) gate driver. Experimental validation demonstrates that the ANN-based MPPT achieves higher power extraction efficiency than conventional perturb-and-observe methods, particularly under rapidly changing mechanical excitation. Furthermore, the approach stabilises the output voltage while maintaining near-maximum power transfer, making it suitable for low-power IoT applications where energy efficiency is critical. The simplicity and robustness of the proposed solution highlight its potential for practical deployment in real-world energy harvesting scenarios.
Keywords: ANN-Based MPPT, Impedance Matching, Buck Converter, PEH
Scope of the Article: Electrical Engineering