Parameter estimation of an unmanned aerial vehicle using dandelion algorithm

Abstract

In this study, we introduce a novel approach to the parameter estimation of Unmanned Aerial Vehicles (UAVs) utilizing the dandelion algorithm, a bio-inspired optimization technique that simulates the seed dispersal mechanism of dandelions. With UAVs increasingly becoming integral to various sectors, accurate parameter estimation emerges as a critical factor in ensuring their optimal performance and safety. Traditional parameter estimation methods often fall short, plagued by computational inefficiencies and a propensity for local optima, which can significantly hinder UAV operations. The dandelion algorithm, with its unique global search capabilities and adeptness in navigating multidimensional spaces, presents a solution that markedly enhances the precision and speed of parameter estimation. Through a series of simulations involving diverse UAV models, this study compares the performance of the dandelion algorithm against the conventional technique; the Particle Swarm Optimization (PSO), demonstrating its superior ability in achieving rapid convergence, higher accuracy, and an exceptional aptitude for avoiding local optima. Our findings not only underscore the algorithm's potential to revolutionize UAV parameter estimation but also highlight its applicability in advancing UAV technology and bio-inspired computational algorithms. This research contributes to the aerospace engineering field by offering an innovative, efficient alternative to existing parameter estimation methods, promising significant improvements in the design, operation, and safety of UAV systems across a spectrum of applications.