ESO based adaptive neural network control for a quadrotor against wind and payload disturbances

This paper investigates the design of a robust controller for the trajectory tracking issue of an underactuated quadrotor unmanned aerial vehicle (UAV) subject to multiple disturbances. An anti-disturbance control framework is proposed by utilizing extended state observer (ESO) and neural network technology. Firstly, the dynamic model of the quadrotor UAV under wind and payload disturbance is established. To actively estimate the lumped disturbance of the UAV system, an ESO with only one parameter is introduced and the disturbances are transformed into the extended state of the UAV system for estimation. Secondly, an adaptive tracking controller that does not accurately obtain the dynamic model knowledge is constructed based on neural network method, where weights of the network can be automatically adjusted by the developed adaptive law. Then, finite-time convergency is analyzed for the ESO with only one parameter, and the Lyapunov criterion is adopted to verify the uniform ultimate boundedness of the UAV closed-loop system. Finally, various simulations under different scenarios are carried out to demonstrate the superiority and effectiveness of the proposed control strategy. For comparison, linear active disturbance rejection control (LADRC), sliding mode control (SMC), model-free based terminal SMC (MFTSMC), and adaptive fractional-order control (ADFOC) algorithms are introduced. Moreover, the physical experiment is given to validate the practicability of the proposed method.