Complex Structural-Parametric Optimization of Fuzzy Control Systems Based on Bioinspired Algorithms

Abstract

This article presents a comprehensive approach to the structural-parametric optimization of Mamdani-type fuzzy control systems (FCS) using a set of bioinspired methods. The proposed methodology integrates optimization of the number of linguistic terms (LT), rule base (RB) synthesis, selection of membership function (MF) types, parametric tuning, as well as the adjustment of fuzzy inference engine (FIE) operations and defuzzification methods in the most rational sequence. The approach is validated through its application to a FCS for an unmanned aerial vehicle (UAV). Experimental results demonstrate that the greatest improvements in control accuracy are achieved through the parametric optimization, with reductions in the objective function of up to 27.5%. The final optimization stage yielded only minor gains (5.6%), indicating its lower impact. The proposed optimization strategy enables the development of high-performance fuzzy systems with simplified implementations and reduced computational costs, making it suitable for embedded control applications in robotics and autonomous systems.