Swarm Intelligence-Based PID Tuning for Drone Control in Photovoltaic Panel Monitoring using Fire Hawk Algorithm

The increasing demand for energy, driven by population growth and the expansion of industrialization, has led to the adoption of a more diversified and sustainable global energy matrix. In this sense, photovoltaic power generation systems have emerged as a versatile and popular renewable energy system. Solar panels, known for their high energy output and low cost, are widely used in both industrial and residential settings. However, maintaining these panels operating at nominal conditions requires regular inspections to identify potential issues such as scratches, erosion, and dust accumulation, which can significantly reduce energy efficiency of these devices. Traditional inspection methods are time-consuming and costly, especially for large installations. The use of drones equipped with cameras for efficient and safe photovoltaic panel inspections is currently gaining attention worldwide. However, a sophisticated control system is necessary to ensure precise and stable drone flight. While traditional PID controllers are commonly used for their simplicity and cost-effectiveness, tuning the PID parameters is challenging for highly dynamical systems such as drones, and they are often designed with suboptimal performance. Therefore, this paper proposes a nature-inspired meta-heuristic algorithm-based controller tuning methodology using the fire hawk optimization. The developed method has systematic rules to ensure that the drone achieves stable and precise flight when using the optimized drone controllers.