Enhanced Trajectory Control of Quadrotor UAV Using Fuzzy PID Based Recurrent Neural Network Controller

This paper presents a novel Fuzzy PID-based Recurrent Neural Network (FPIDRNN) controller designed to enhance trajectory control in quadrotor Unmanned Aerial Vehicles (UAVs). Conventional control approaches such as Proportional-Integral-Derivative (PID) and Fuzzy PID (FPID) require extensive tuning and a deep understanding of system dynamics. On the other hand, the FPIDRNN controller provides a data-driven alternative that adapts effectively to complex, nonlinear behaviors without the need for explicit dynamic modeling. By training the neural network to replicate the control actions of the Fuzzy PID controller, this approach combines the strengths of fuzzy logic and neural adaptation, resulting in robust and precise control. A comparative analysis of three controllers—PID, Fuzzy PID (FPID), and the proposed FPIDRNN—is conducted in the context of quadrotor UAV control. Extensive numerical simulations demonstrate that the FPIDRNN controller significantly enhances tracking accuracy, robustness, and adaptability compared to both the PID and FPID controllers, as evidenced by various performance indices. This paper underscores the efficiency of the FPIDRNN as an advanced control solution, integrating fuzzy logic, PID, and neural network techniques to elevate UAV control systems.