Enhancing Intelligent Control Strategies for UAVs: A Comparative Analysis of Fuzzy Logic, Fuzzy PID, and GA-Optimized Fuzzy PID Controllers

Abstract

This paper presents advanced control strategies to enhance the stability and trajectory tracking performance of quadrotor systems. The study investigates three control methodologies: the Fuzzy Logic Controller (Fuzzy), the Fuzzy Proportional-Integral-Derivative (FPID) controller, and the Genetic Algorithm (GA)-optimized Fuzzy PID controller (GAFPID). The Fuzzy controller leverages heuristic rules for adaptive control, while the FPID controller integrates conventional PID dynamics with fuzzy logic to improve precision and robustness. The GAFPID controller employs evolutionary computation through a genetic algorithm to optimize parameter tuning, offering superior control performance. Comparative simulations are conducted under diverse operating conditions, including external disturbances and parameter variation scenarios, with performance evaluated using the Integral of Time-weighted Absolute Error (ITAE) metric. Results demonstrate that the GAFPID controller outperforms the other approaches in terms of precision, adaptability, and robustness, establishing it as a promising solution for complex quadrotor applications.

Keywords

• FPID
• GAFPID
• fuzzy
• unmanned aerial vehicle (UAV)