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To satisfy the lightweight requirements of large pipe weapons, a novel electrohydraulic servo (EHS) system where the hydraulic cylinder possesses three cavities is developed and investigated in the present study. In the EHS system, the balancing cavity of the EHS is especially designed for active compensation for the unbalancing force of the system, whereas the two driving cavities are employed for positioning and disturbance rejection of the large pipe. Aiming at simultaneously balancing and positioning of the EHS system, a novel neural network based active disturbance rejection control (NNADRC) strategy is developed. In the NNADRC, the radial basis function (RBF) neural network is employed for online updating of parameters of the extended state observer (ESO). Thereby, the nonlinear behavior and external disturbance of the system can be accurately estimated and compensated in real time. The efficiency and superiority of the system are critically investigated by conducting numerical simulations, showing that much higher steady accuracy as well as system robustness is achieved when comparing with conventional ADRC control system. It indicates that the NNADRC is a very promising technique for achieving fast, stable, smooth, and accurate control of the novel EHS system.

To achieve perfect behavior of the unbalanced barrel of a gun control system, a novel control strategy for simultaneous balancing and positioning of the system is proposed, physically being on the basis of a novel hydraulic cylinder with three cavities. The fuzzy fractional order proportional-integral-derivative controller is developed, and the particle swarm optimization algorithm is adopted for optimal selection of the control parameters for the gun control system. The results demonstrate that the fuzzy fractional order proportional-integral-derivative control strategy can finely improve dynamic performance of the control system, and the nonlinear characteristics of system can be effectively suppressed. KeywordsGun control system, balancing and positioning, fuzzy fractional order proportional-integral-derivative, particle swarm optimization algorithm Date

Unmanned aerial vehicles (UAVs) are important in modern war, and object detection performance influences the development of related intelligent drone application. At present, the target categories of UAV detection tasks are diversified. However, the lack of training samples of novel categories will have a bad impact on the task. At the same time, many state-of-the-arts are not suitable for drone images due to the particularity of perspective and large number of small targets. In this paper, we design a fast few-shot detector for drone targets. It adopts the idea of anchor-free in fully convolutional one-stage object detection (FCOS), which leads to a more reasonable definition of positive and negative samples and faster speed, and introduces Siamese framework with more discriminative target model and attention mechanism to integrate similarity measures, which enables our model to match the objects of the same categories and distinguish the different class objects and background. We propose a matching score map to utilize the similarity information of attention feature map. Finally, through soft-NMS, the predicted detection bounding boxes for support category objects are generated. We construct a DAN dataset as a collection of DOTA and NWPU VHR-10. Compared with many state-of-the-arts on the DAN dataset, our model is proved to outperform them for few-shot detection tasks of drone images.

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