Active Disturbance Rejection Generalized Predictive Control of a Quadrotor UAV via Quantitative Feedback Theory

Abstract

This paper investigates the design of a robust controller for a quadrotor unmanned aerial vehicle (UAV) system subject to parameter uncertainties and external disturbances. A new double closed-loop active disturbance rejection generalized predictive control (ADRC-GPC) is proposed for the trajectory tracking problem of the UAV. Considering the measurement noise, the ADRC-GPC control strategy can provide better dynamic performance and robustness against uncertainties and external disturbances than active disturbance rejection control (ADRC). Furthermore, a controller parameter tuning method for ADRC-GPC is proposed. In the framework of the classical two-degree-of-freedom equivalent model, the bandwidth of ESO is determined according to the parameter selection criterion. The criterion is a trade-off between the estimation accuracy and the immunity of measurement noise. In addition, the parameters of the ADRC-GPC controller are tuned by quantitative feedback theory to achieve the expected performance specifications. Finally, the proposed method is compared with several traditional control methods in the simulation experiments. The simulation results show that the proposed method has better set-point tracking performance, disturbance rejection performance, and robustness.