Guidance and Control for UAV Aerial Refueling Docking Based on Dynamic Inversion with L_1 Adaptive Augmentation

The guidance and control for UAV aerial refueling docking based on dynamic inversion with L1 adaptive augmentation is studied.In order to improve the tracking performance of UAV aerial refueling docking,aguidance algorithm is developed to satisfy the tracking requirement of position and velocity,and it generates the UAV flight control loop commands.In flight control loop,based on the 6-DOF nonlinear model,the angular rate loop and the attitude loop are separated based on time-scale principle and the control law is designed using dynamic inversion.The throttle control is also derived from dynamic inversion method.Moreover,an L1 adaptive augmentation is developed to compensate for the undesirable effects of modeling uncertainty and disturbance.Nonlinear digital simulations are carried out.The results show that the guidance and control system has good tracking performance and robustness in achieving accurate aerial refueling docking.

Importance of Adaptive Photometric Augmentation for Different Convolutional Neural Network

Existing segmentation and augmentation techniques on convolutional neural network(CNN)has produced remarkable progress in object detection.However,the nominal accuracy and performance might be downturned with the photometric variation of images that are directly ignored in the training process,along with the context of the individual CNN algorithm.In this paper,we investigate the effect of a photometric variation like brightness and sharpness on different CNN.We observe that random augmentation of images weakens the performance unless the augmentation combines the weak limits of photometric variation.Our approach has been justified by the experimental result obtained from the PASCAL VOC 2007 dataset,with object detection CNN algorithms such as YOLOv3(You Only Look Once),Faster R-CNN(Region-based CNN),and SSD(Single Shot Multibox Detector).Each CNN model shows performance loss for varying sharpness and brightness,ranging between−80%to 80%.It was further shown that compared to random augmentation,the augmented dataset with weak photometric changes delivered high performance,but the photometric augmentation range differs for each model.Concurrently,we discuss some research questions that benefit the direction of the study.The results prove the importance of adaptive augmentation for individual CNN model,subjecting towards the robustness of object detection.

Robust adaptive control of hypersonic vehicle considering inlet unstart

In this paper,a model reference adaptive control(MRAC)augmentation method of a linear controller is proposed for air-breathing hypersonic vehicle(AHV)during inlet unstart.With the development of hypersonic flight technology,hypersonic vehicles have been gradually moving to the stage of weaponization.During the maneuvers,changes of attitude,Mach number and the back pressure can cause the inlet unstart phenomenon of scramjet.Inlet unstart causes significant changes in the aerodynamics of AHV,which may lead to deterioration of the tracking performance or instability of the control system.Therefore,we firstly establish the model of hypersonic vehicle considering inlet unstart,in which the changes of aerodynamics caused by inlet unstart is described as nonlinear uncertainty.Then,an MRAC augmentation method of a linear controller is proposed and the radial basis function(RBF)neural network is used to schedule the adaptive parameters of MRAC.Furthermore,the Lyapunov function is constructed to prove the stability of the proposed method.Finally,numerical simulations show that compared with the linear control method,the proposed method can stabilize the attitude of the hypersonic vehicle more quickly after the inlet unstart,which provides favorable conditions for inlet restart,thus verifying the effectiveness of the augmentation method proposed in the paper.