Fractional order adaptive robust formation control of multiple quad-rotor UAVs with parametric uncertainties and wind disturbances

In recent times, multiple Unmanned Aerial Vehicles (UAVs) are being widely utilized in several areas of applications such as agriculture, surveillance, disaster management, search and rescue operations. Degree of robustness of applied control schemes determines how accurate a swarm of UAVs accomplish group tasks. Formation and trajectory tracking controllers are required for the swarm of multiple UAVs. Factors like external environmental effects, parametric uncertainties and wind gusts make the controller design process as a challenging task. This article proposes fractional order formation and trajectory tacking controllers for multiple quad-rotors using Super Twisting Sliding Mode Control (STSMC) technique. To compensate the effects of the disturbances due to parametric uncertainties and wind gusts, Lyapunov function based adaptive controllers are formulated. Moreover, Lyapunov theorem is used to guarantee the stability of the proposed controllers. Three types of controllers, namely fixed gain STSMC and fractional order Adaptive Super Twisting Sliding Mode Control (ASTSMC) methods are tested for the swarm of UAVs by performing the numerical simulations in MATLAB/Simulink environment. From the presented results, it is verified that in presence of wind disturbances and parametric uncertainties, the proposed fractional order ASTSMC technique showed improved robustness as compared to the fixed gain STSMC and integer order ASTSMC.     

Conditional self-attention generative adversarial network with differential evolution algorithm for imbalanced data classification

Imbalanced data classification is an important research topic in real-world applications, like fault diagnosis in an aircraft manufacturing system. The over-sampling method is often used to solve this problem. It generates samples according to the distance between minority data. However, the traditional over-sampling method may change the original data distribution, which is harmful to the classification performance. In this paper, we propose a new method called Conditional Self-Attention Generative Adversarial Network with Differential Evolution (CSAGAN-DE) for imbalanced data classification. The new method aims at improving the classification performance of minority data by enhancing the quality of the generation of minority data. In CSAGAN-DE, the minority data are fed into the self-attention generative adversarial network to approximate the data distribution and create new data for the minority class. Then, the differential evolution algorithm is employed to automatically determine the number of generated minority data for achieving a satisfactory classification performance. Several experiments are conducted to evaluate the performance of the new CSAGAN-DE method. The results show that the new method can efficiently improve the classification performance compared with other related methods.     

对系统辨识误差鲁棒的自适应复合控制方法研究

对基于系统辨识的自校正控制方法来说,其控制性能极大程度受制于辨识精度。针对这一缺点,提出了将变结构模型参考自适应控制与自校正控制相结合的自适应复合控制方法。以二阶线性对象为例,首先给出了极点配置理论下的自校正控制方法,并分析了辨识误差对控制性能的影响,对这一问题,引入变结构自适应控制,并给出了自适应控制律,最后通过仿真证明了这种复合控制方法既能抑制辨识误差的影响,同时也保留了自校正控制参数调整迅速准确的优点。     

针对弹载卫星导航接收机干扰方向测定的算法研究

在弹载应用中,卫星导航接收机动态范围大,干扰方向相对接收机变化迅速,高动态导致自适应抗干扰算法产生的零陷深度变浅、范围变大,同时破坏了零陷附近接收的卫星信号,严重影响了 自适应空时滤波的抗干扰性能.研究弹载卫星导航接收机干扰方向的测定方法,通过干扰测向将天线零陷对准干扰方向,在指定方向形成波束,从而提高了接收机的抗干扰能力.针对弹载高动态对卫星导航接收机干扰方向测定实时性和准确度的要求,提出了 MUSIC-ESPRIT联合算法,将传统的ESPRIT算法的测定结果作为MUSIC算法的初始值,进而缩小谱峰的搜索范围.仿真结果表明,提出的MUSIC-ESPRIT联合算法与传统的MUSIC算法相比,在保证干扰测向精度的同时降低了计算量,满足弹载卫星导航接收机对干扰方向的测定要求.     

空间仿生可变构智能感知网络技术

基于仿生集群系统感知功能与行为的视角，提出了空间感知网络的若干前沿科学问题，包括仿生可变构异构空间分布式智能感知网络设计、单星自主机动对准和协调操控、星群协同相对测量与控制等。在干扰对抗态势下，空间感知网络的生存智能需求是保持各节点的可变构型网络、异构分布式感知与协调控制，从抗扰、容错和节能等角度提出了未来智能感知网络所应具有的安全、绿色和免疫等特征。仿生空间感知网络的目标是通过可变构异构分布式星群设计，实现星群多源信息融合和“眼、耳、脑、体、群”的智能协调，提高感知网络的智能协调能力以及对于空地目标与空间态势的感知、理解、预判和机动处置能力。     

Review on residual stress and its effects on manufacturing of aluminium alloy structural panels with typical multi-processes

In the aerospace industry, integrated aluminium alloy plates and stiffened panels with high accuracy and performance attract significant interest. To manufacture these panels as integrity with high accuracy, multiple processes need to be utilised, such as machining, welding and forming. During the whole manufacturing chain, residual stresses can be generated and redistributed in the components among different processes. The residual stress would significantly affect the shapes and properties of the final products. Currently, these great effects are not well considered in the design and manufacturing processes. This paper aims to draw a general understanding of the residual stress generated in the pre-manufacturing processes and its effects on subsequent manufacturing processes. The mechanisms and distributions of residual stresses generated in typical pre-manufacturing processes of structural panels, including machining, welding and additive manufacturing (AM), are firstly summarised. The detailed effects of generated residual stresses on distortion and application properties in subsequent manufacturing processes are then concluded. In addition, current methods developed for the investigation of residual stress effect in multi-processes manufacturing are critically reviewed, including experimental, analytical, finite element (FE) and machine learning methods. Furthermore, the future development trend of methods for residual stress consideration and control in the design of manufacturing processes is summarised, providing comprehensive guidance to achieve the high accurate manufacturing of aluminium alloy structural components.