Bio-inspired optimization of fuzzy control system for inspection robotic platform: comparative analysis of hybrid swarm methods

Abstract

The focus of this paper is to address research issues and conduct a comparative analysis of swarm bio-inspired methods for optimizing parameters in fuzzy systems (FS). Various hybrid modifications of particle swarm optimization (PSO) and grey wolf optimization (GWO) multi-agent techniques, tailored for FS parameter optimization, are compared with conventional search methods. The research and comparative analysis are performed using a specific example: the parametric optimization of a Takagi-Sugeno fuzzy control system designed for an inspection mobile robotic platform (MRP). This robotic platform is capable of navigating inclined and vertical surfaces. It serves as an efficient autonomous tool for executing complex inspection and monitoring tasks in challenging and hazardous environments in various industrial facilities and urban environments inaccessible to humans. However, to effectively utilize this MRP, an intelligent control system optimized through an advanced method is necessary. The simulation results obtained validate the effectiveness of the presented swarm bio-inspired optimization techniques, considering both the performance achieved by the FS and the computational costs incurred. © 2024 CEUR-WS.