Unmanned aerial manipulator (UAM) is usually a combination of a quadrotor and a robotic arm that can exert active influences on the environments. The control problems of the UAM system include model uncertainty caused by its center of gravity shift and external disturbances from the environments. To handle these two disturbances, a tracking control strategy is proposed for position and attitude control of the UAM in this paper. In particular, the model of the UAM is established considering with center of gravity shift and disturbances from environments. In the position control, both internal disturbances and external disturbances are compensated by using a sliding mode controller. In the attitude control, an adaptive law is designed to estimate internal disturbances, and a disturbance observer is designed to estimate external disturbances. The stability analysis of the proposed controller is provided and the effectiveness of the proposed method is verified in simulation.

This paper devotes to the adaptive globally synchronization within predefined-time of two time-delayed fractional-order chaotic systems. Firstly, through fractional calculus, two novel different fractional-order systems with time-delay are proposed, whose convergence is guaranteed and phase trajectory is given. Secondly, by exploiting the non-negative Lyapunov function and inequality theorem, a novel global predefined-time stability theorem is proposed, which can ensure the settling time tunable. And the upper bound of the settling time estimation is more accurate compared with the classical results. With the help of novel predefined-time stability theorem, two active controllers are designed, namely the fixed-time synchronization controller and predefined-time synchronization controller, to achieve the fixed-time synchronization and the predefined-time synchronization of two different time-delayed fractional-order chaotic systems respectively. Finally, several numerical simulations are presented in order to show the effectiveness of the proposed methods.

This paper concentrates on the global predefined-time synchronization of delayed memristive neural networks with external unknown disturbance via an observer-based active control. First, a global predefined-time stability theorem based on a non-negative piecewise Lyapunov function is proposed, which can obtain more accurate upper bound of the settling time estimation. Subsequently, considering the delayed memristive neural networks with disturbance, a disturbance-observer is designed to approximate the external unknown disturbance in the response system with a Hurwitz theorem and then to eliminate the influence of the unknown disturbance. With the help of global predefined-time stability theorem, the predefined-time synchronization is achieved between two delayed memristive neural networks via an active control Lyapunov function design. Finally, two numerical simulations are performed, and the results are given to show the correctness and feasibility of the predefined-time stability theorem.

In this article, an integrated object detection and communication (IODC) system based on single-stage detection framework is first proposed for synthetic aperture radar (SAR) images in underwater environment. Specifically, the combination of the multiscale feature extraction module and semantic information enhance fusion module for SAR images in underwater environment is designed as the semantic encoder and the results prediction module with anchor-free detection method is designed as the semantic decoder. Considering the multiscale feature, the channel encoder composed by a multiscale fusion module and a redundancy module is designed, and the channel decoder is the inverse of the channel encoder. To adapt to the time-varying and complex wireless environment, an adaptive transmission (AT) mechanism based on attention mechanism and knowledge base is proposed for the IODC system. Moreover, considering the actual application requirements, a lightweight design for the IODC system with the AT mechanism is also conducted. The experiment results on the Sonar Common Target Detection dataset show that the proposed IODC system with AT mechanism can achieve nearly 91% average precision at 25 dB, which means the proposed system can achieve the effective integration of the objection detection and communication for SAR images in underwater environment. In the lightweight design, the model parameters of the proposed system can be reduced by up to 20%, with only a 2.22% sacrifice in performance.

In actual traffic scenarios, the environment is complex and constantly changing, with many vehicles that have substantial similarities, posing significant challenges to vehicle tracking research based on deep learning. To address these challenges, this article investigates the application of the DeepSORT (simple online and realtime tracking with a deep association metric) multitarget tracking algorithm in vehicle tracking. Due to the strong dependence of the DeepSORT algorithm on target detection, a YOLOv5s_DSC vehicle detection algorithm based on the YOLOv5s algorithm is proposed, which provides accurate and fast vehicle detection data to the DeepSORT algorithm. Compared to YOLOv5s, YOLOv5s_DSC has no more than a 1% difference in optimal mAP0.5 (mean average precision), precision rate, and recall rate, while reducing the number of parameters by 23.5%, the amount of computation by 32.3%, the size of the weight file by 20%, and increasing the average processing speed of each image by 18.8%. After integrating the DeepSORT algorithm, the processing speed of YOLOv5s_DSC + DeepSORT reaches up to 25 FPS, and the system exhibits better robustness to occlusion.