Swarm optimization of the drone s intelligent control system: comparative analysis of hybrid techniques

Abstract

Over recent years, bioinspired swarm techniques have gained significant popularity for addressing real-world engineering optimization challenges. One promising application of these methods is developing and optimizing of intelligent systems, specifically fuzzy control systems. This paper examines research issues and performs a comparative analysis of bioinspired swarm methods for parameter optimization in fuzzy control systems. It compares various hybrid modifications of particle swarm optimization and grey wolf optimization techniques, specifically adapted for fuzzy system parameter optimization, against traditional search methods. As a case study, the paper uses the parametric optimization of a Takagi-Sugeno fuzzy control system designed for a quadrotor-type unmanned aerial vehicle (UAV). The simulation results confirm the effectiveness of the presented swarm bioinspired optimization techniques, taking into account both the performance of the UAV's fuzzy control system and the computational costs involved.