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This article uses bio-inspired algorithms to optimize PID controllers in solar tracking systems, inspired by phototropism in sunflowers. Bio-mimicry can improve system responsiveness and efficiency, therefore the paper examines six popular algorithms: genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), flower pollination algorithm (FPA), grey wolf optimizer (GWO), and cray fish optimization algorithm (COA). To determine the optimal controller parameters, the integral of squared error (ISE) is used as the objective function and the integral of absolute error (IAE), integral of time-weighted absolute error (ITAE), and mean squared error (MSE) are evaluated. The study investigates system time response characteristics such settling time, peak overshoot, peak time, and rising time to evaluate each algorithm's controller gain optimization. Results show that bio-inspired algorithms, which allow adaptive tuning, regularly outperform standard approaches in solar PID cell orientation for optimum sunlight collection. The selected bio-inspired algorithms function similarly, demonstrating their usefulness in improving solar tracking system performance and dependability.

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Optimal PID Tuning for Solar Tracking System Using Bio-Inspired Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Varikuti Sai Soumya Reddy,
  • U. Kruthika,
  • Surekha Paneerselvam

摘要

This article uses bio-inspired algorithms to optimize PID controllers in solar tracking systems, inspired by phototropism in sunflowers. Bio-mimicry can improve system responsiveness and efficiency, therefore the paper examines six popular algorithms: genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), flower pollination algorithm (FPA), grey wolf optimizer (GWO), and cray fish optimization algorithm (COA). To determine the optimal controller parameters, the integral of squared error (ISE) is used as the objective function and the integral of absolute error (IAE), integral of time-weighted absolute error (ITAE), and mean squared error (MSE) are evaluated. The study investigates system time response characteristics such settling time, peak overshoot, peak time, and rising time to evaluate each algorithm's controller gain optimization. Results show that bio-inspired algorithms, which allow adaptive tuning, regularly outperform standard approaches in solar PID cell orientation for optimum sunlight collection. The selected bio-inspired algorithms function similarly, demonstrating their usefulness in improving solar tracking system performance and dependability.