Optimizing Energy Efficiency in Agricultural Spraying Drones Through Fractional-Order PID Controllers and Genetic Algorithm

The viability of precision agriculture in family farming is intrinsically linked to the operational and energy efficiency of equipment, especially when relying on on-site renewable sources like photovoltaic panels. This paper addresses the challenge of maximizing the energy efficiency of spraying drones through advanced control optimization. The methodology employs a Genetic Algorithm (GA) to optimize and compare two controller types: a conventional Proportional-Integral-Derivative (PID) and a Fractional-Order PID (FOPID). The optimization aims to minimize a cost function that models energy consumptionand flight time. Simulation results demonstrate that the FOPID controller achieves a 3.1% reduction in operational cost per hectare compared to the conventional PID. Analysis of the acceleration profiles reveals that the FOPID, despite exhibiting high-frequency behavior, avoids the large-amplitude peaks characteristic of the PID, resulting in lower overall control effort and, consequently, reduced energy consumption. The study concludes that advanced software optimization, specifically with FOPID controllers, offers an energy-efficient solution that promotes sustainability in family farming.