Active Disturbance Rejection Control of Quadrotor UAVs Based on Joint Observation and Feedforward Compensation北大核心CSCD

为解决模型参数不确定与外界干扰影响下，四旋翼无人机飞控作业中姿态与轨迹跟踪精度下降，反应迟缓的问题，利用拓展Kalman滤波应对非线性系统问题出色的适应能力和噪声抑制能力，对四旋翼状态信息进行初步估算来抑制高频信号干扰，从而降低了扩张状态观测器的估计负担.同时，与扩张状态观测器联合估计由系统不确定性参数与外界扰动联合组成的“总扰动”，使系统对于精确模型的依赖性降低，并利用扰动估计的微分值进行前馈补偿，以提高对突变扰动的跟踪精度，克服了突变干扰下的相位滞后现象.综合联合观测器、带前馈补偿的LESO及带误差补偿的PD控制律，形成了一种利用拓展Kalman滤波与前馈补偿后的扩张状态观测器联合观测扰动，能较大程度抑制高频噪声和突变扰动的改进型自抗扰控制器.仿真与实验结果表明，联合观测器能有效地减小观测误差幅值且能超前校正观测相位滞后，从而更好地得到更精确的状态信息，改进型自抗扰控制器能更好地满足四旋翼飞行器快速反应、高效稳定的控制要求，精准高效地完成复杂轨迹跟踪.

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.