Fuzzy Adaptive Control Law for Trajectory Tracking Based on a Fuzzy Adaptive Neural PID Controller of a Multi-rotor Unmanned Aerial Vehicle

This article presents a fuzzy adaptive control law (FACL) designed for tracking the trajectory of a low-scale unmanned aerial vehicle (UAV), based on a new fuzzy adaptive neural proportional integral derivative (FANPID) controller. FACL estimates the angles of rotation, if the reference trajectory is proposed, applying the adaptivity of the new FANPID-Lyapunov controller. UAV parameters were previously identified using the fuzzy adaptive neurons (FAN) method and experimental aerodynamic data. FANPID-Lyapunov controller optimizes trajectory tracking and stability analysis is performed. The FACL simulation results obtained in Matlab (R)/Simulink show the effectiveness, adaptivity and optimization of the flight control system, because it self-tunes the angles satisfactorily, adapts the gains and parameter for the FANPID-Lyapunov-Fuzzy controller, and reduces the error considerably compared to the controllers PID-Fixed gains, PID-Fuzzy adaptive gains, PID-Lyapunov-Fixed gains, and FOPID-Lyapunov-Fuzzy adaptive gains and parameters.

Automated summary

This article introduces a fuzzy adaptive control law (FACL) for trajectory tracking of a small-scale UAV using a new fuzzy adaptive neural proportional integral derivative (FANPID) controller. The FACL uses the adaptivity of the FANPID-Lyapunov controller to estimate rotation angles when a reference trajectory is provided. The UAV parameters are identified using the fuzzy adaptive neurons (FAN) method and experimental aerodynamic data. The FANPID-Lyapunov controller optimizes trajectory tracking and stability analysis is performed. Simulation results in Matlab (R)/Simulink show the effectiveness, adaptivity, and optimization of the flight control system, reducing error considerably compared to other controllers.