A Robust Disturbance-Rejection Controller Using Model Predictive Control for Quadrotor UAV in Tracking Aggressive Trajectory

Abstract

A robust controller for the waypoint tracking of a quadrotor unmanned aerial vehicle (UAV) is proposed in this paper, in which position control and attitude control are effectively decoupled. Model predictive control (MPC) is employed in the position controller. The constraints of motors are imposed on the state and input variables of the optimization equation. This design effectively mitigates the nonlinearity of the attitude loop and enhances the planning efficiency of the position controller. The attitude controller is designed using a nonlinear and robust control law based on SO(3) space, which enables continuous control on the SO(3) manifold. By extending the differential flatness of the quadrotor-UAV to the angular acceleration level, the mapping of the control reference from the position controller to the attitude controller is achieved. Simulations are carried out to demonstrate the capability of the proposed controller. In the simulations, multiple aggressive flight trajectories and severe external disturbances are designed. The results show that the controller is robust, with superior accuracy in tracking aggressive trajectories.

Design, Modeling, and Control of a Composite Tilt-Rotor Unmanned Aerial Vehicle

“…Although using the method in Section 4.2 for transition flight may not necessarily be the most fuel efficient, it is stable and reliable in practice, while also expanding the transition envelope of the drone to a certain extent. Due to the external disturbances experienced by the aircraft during flight, we add white noise with a sampling frequency of 0.1 s and an amplitude of 0.1 to the attitude measurement values to simulate the observation errors of IMU sensors; We also add white noise with a sampling frequency of 0.1 s and an amplitude of 0.05 to the measured velocity to simulate GPS measurement errors [33]. The remaining configurations align with the simulation that does not involve any disturbance.…”

Design and Real-Time Implementation of a Cascaded Model Predictive Control Architecture for Unmanned Aerial Vehicles

“…Moreover, considering linear control techniques are widely used for UAV flight control [28,30,31,34,39], as they can ensure optimal performance when operating close to the equilibrium point [40,41], a linearization of the model under the following assumptions is carried out:…”

“…Here, the computationally efficient control architecture for a multivariable system is obtained, which reduces the complexity of the system dynamics and improves its real-time performance [13,33]. The overall MPC strategy is decomposed into two different schemes [16,34]. The first scheme controls the translational displacements while the second scheme regulates the rotational movements of the quadrotor.…”

Optimization-Based Control for a Large-Scale Electrical Vertical Take-Off and Landing during an Aircraft’s Vertical Take-Off and Landing Phase with Variable-Pitch Propellers

“…Meanwhile, large-scale aircraft pay more attention to subjects like minimizing energy consumption and stabilizing the overall flight process, where the above control framework is not suitable to be directly applied. Xu et al [12] introduced an aggressive trajectory tracker based on MPC and a nonlinear attitude controller for quadrotors, which is utilized on quadrotor platforms without limiting energy consumption. Unlike tiny quadrotors which are able to sacrifice some targets in order to elegantly track some aggressive trajectories, large eVTOLs should consider many more aspects of the flight, including the robustness against external disturbances and measurement noise, and energy consumption analysis.…”