Cascaded Fuzzy PID Control for Quadrotor UAVs Based on RBF Neural Networks

Since quadrotor UAVs often need to fly in complex and changing environments, their systems suffer from slow smooth control response, weak self-turbulence capability, and poor self-adaptability. Thus, it is crucially important to carefully formulate a quadrotor UAV control system that can maintain high-precision control and high immunity to disturbance in complex environments. In this paper, an improved nonlinear cascaded fuzzy PID control approach for quadrotor UAVs based on RBF neural network is proposed. Based on the analysis and establishment of the UAV flight control model, this paper designs a control approach with an outer-loop fuzzy adaptive PID control and an inner-loop RBF neural network. The simulation results show that introducing RBF neural networks into the nonlinear fuzzy adaptive PID control can make it have better high-precision control and high anti-disturbance under the influence of different environmental variables.