In this paper, a robust adaptive sliding mode control scheme is developed for attitude and altitude tracking of a quadrotor unmanned aerial vehicle (UAV) system under the simultaneous effect of parametric uncertainties and consistent external disturbance. The underactuated dynamic model of the quadrotor UAV is first built via the Newton–Euler formalism. Considering the nonlinear and strongly coupled characteristics of the quadrotor, the controller is then designed using a sliding mode approach. Meanwhile, additional adaptive laws are proposed to further improve the robustness of the proposed control scheme against the parametric uncertainties of the system. It is proven that the control laws can eliminate the altitude and attitude tracking errors, which are guaranteed to converge to zero asymptotically, even under a strong external disturbance. Finally, numerical simulation and experimental tests are performed, respectively, to verify the effectiveness and robustness of the proposed controller, where its superiority to linear quadratic control and active disturbance rejection control has been demonstrated clearly.

In this paper, a CNN-based learning scheme is proposed to enable a quadrotor unmanned aerial vehicle (UAV) to avoid obstacles automatically in unknown and unstructured environments. In order to reduce the decision delay and to improve the robustness for the UAV, a two-stage end-to-end obstacle avoidance architecture is designed, where a forward-facing monocular camera is used only. In the first stage, a convolutional neural network (CNN)-based model is adopted as the prediction mechanism. Utilizing three effective operations, namely depthwise convolution, group convolution and channel split, the model predicts the steering angle and the collision probability simultaneously. In the second stage, the control mechanism maps the steering angle to an instruction that changes the yaw angle of the UAV. Consequently, when the UAV encounters an obstacle, it can avoid collision by steering automatically. Meanwhile, the collision probability is mapped as a forward speed to maintain the flight or stop going forward. The presented automatic obstacle avoidance scheme of quadrotor UAV is verified by several indoor/outdoor tests, where the feasibility and efficacy have been demonstrated clearly. The novelties of the method lie in its low sensor requirement, light-weight network structure, strong learning ability and environmental adaptability.

Control and path planning are two essential and challenging issues in quadrotor unmanned aerial vehicle (UAV). In this paper, an approach for moving around the nearest obstacle is integrated into an artificial potential field (APF) to avoid the trap of local minimum of APF. The advantage of this approach is that it can help the UAV successfully escape from the local minimum without collision with any obstacles. Moreover, the UAV may encounter the problem of unreachable target when there are too many obstacles near its target. To address the problem, a parallel search algorithm is proposed, which requires UAV to simultaneously detect obstacles between current point and target point when it moves around the nearest obstacle to approach the target. Then, to achieve tracking of the planned path, the desired attitude states are calculated. Considering the external disturbance acting on the quadrotor, a nonlinear disturbance observer (NDO) is developed to guarantee observation error to exponentially converge to zero. Furthermore, a backstepping controller synthesized with the NDO is designed to eliminate tracking errors of attitude. Finally, comparative simulations are carried out to illustrate the effectiveness of the proposed path planning algorithm and controller.

No abstract

No abstract