Active Disturbance Rejection Control of Quadrotor UAVs Based on Joint Observation and Feedforward Compensation

Under the effects of uncertain parameters and external disturbances, the attitude and trajectory tracking accuracy will be reduced and the response will be slowed down in the flight control of quadrotor unmanned air vehicles (UAVs). To solve this problem, the extended Kalman filter method was used given its excellent adaptability and noise suppression ability for nonlinear systems, to preliminarily estimate the quadrotor state information and suppress the high-frequency signal disturbance to reduce the estimation burden on the extended state observer. Moreover, the extended Kalman filter combined with the expanded state observer was applied to estimate the total disturbance composed of the system uncertainty parameters and external disturbances to reduce system reliance on precise models, and the differential values of the perturbation estimates were used for feedforward compensation to improve the tracking accuracy under abrupt disturbances and to overcome the phase lag caused by abrupt disturbances. The joint state observer, the linear extended state observer with feedforward compensation and the PD controller with error compensation were integrated to form an improved active disturbance rejection controller to jointly observe disturbances while suppressing high-frequency noises and abrupt disturbances to a relatively large extent, by means of the extended Kalman filter and the extended state observer with feedforward compensation. Simulation and experiment results show that, the joint observer can effectively reduce the observation error amplitude, correct the observation phase lag in advance and obtain more accurate state information, and the improved active disturbance rejection controller can better meet requirements of quadrotor UAVs for fast responses and stable control, and accurately and efficiently fulfill complex trajectory tracking tasks.