Abstract
In this article, a cascade fuzzy neural network (FNN) control approach is proposed for position control of quadrotor unmanned aerial vehicle (UAV) system with high coupling and underactuated. For the attitude loop with limited range, the FNN controller parameters were trained offline using flight data, whereas for the position loop, the method based on FNN compensation proportional-integral-derivative (PID) was adopted to tune the system online adaptively. This method not only combined the advantages of fuzzy systems and neural networks but also reduced the amount of calculation for cascade neural network control. Simulations of fixed set point flight and spiral and square trajectory tracking flight were then conducted. The comparison of the results showed that our method had advantages in terms of minimizing overshoot and settling time. Finally, flight experiments were carried out on a DJI Tello quadrotor UAV. The experimental results showed that the proposed controller had good performance in position control.
Highlights
Cascade Fuzzy Neural Network.The quadrotor unmanned aerial vehicle (UAV) plays an important role as a flying platform
Researchers have developed a significant number of control methods to deal with UAVs, including proportional integral derivative (PID) controller, linear quadratic regulator (LQR) [4], nonlinear [5] sliding mode controller [6], backstepping, fuzzy logic, adaptive neural networks, reinforcement learning, and so on [7]
To deal with the lack of modeling and flight uncertainties, Erdal Kayacan et al used a learning controller composed of a fuzzy neural network in parallel with a conventional proportional controller for control and guidance of a fixed-wing UAV, and the simulation results showed efficiency in a real-time system [24]
Summary
[13], Guiqiu Liao and Jiankang Zhao presented a method based on neural network mapping for the cascade PID parameters tuning Their experiment data only demonstrated the efficiency of this method in altitude control while ignoring the effect of horizontal position. To deal with the lack of modeling and flight uncertainties, Erdal Kayacan et al used a learning controller composed of a fuzzy neural network in parallel with a conventional proportional controller for control and guidance of a fixed-wing UAV, and the simulation results showed efficiency in a real-time system [24]. This paper will try to develop a different cascade fuzzy neural network control strategy to realize the accurate position control of quadrotor UAV.
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