面向智能空战的它机协同试飞验证方法

为验证无人机自主协同算法在空战环境下的适用性，提出了一种高等效的它机协同试飞验证方法。根据算法功能实现的需求，以成熟的民用固定翼无人机平台为基础进行改装，搭建它机试飞平台，对真实空战环境开展模拟和等效设计。以四机协同编队算法为例，在试飞平台上移植算法程序，开展相关科目试飞验证。当需要验证不同控制算法时，无需针对它机试飞平台开发控制策略，只需修改控制算法即可。试验结果表明：编队综合误差较小，算法能够实现无人机编队的稳定控制；同时，它机协同试飞验证方法因其迭代速度快、安全稳定性高、成本低等特点，可用作中间阶段算法的前置试飞手段，为算法的开发迭代提供有效验证。     

Nonlinear adaptive flight control system: Performance enhancement and validation

The problem of decreasing stability margins in L₁ adaptive control systems is discussed and an out-of-loop L₁ adaptive control scheme based on Lyapunov’s stability theorem is proposed. This scheme enhances the effectiveness of the adaptation, which ensures that the system has sufficient stability margins to achieve the desired performance under parametric uncertainty, additional delays, and actuator faults. The stability of the developed control system is demonstrated through a series of simulations. Compared with an existing control scheme, the constant adjustment of the stability margins by the proposed adaptive scheme allows their range to be extended by a factor of 4–5, bringing the stability margin close to that of variable gain PD control with adaptively scheduled gains. The engineered practicability of adaptive technology is verified. A series of flight tests verify the practicability of the designed adaptive technology. The results of these tests demonstrate the enhanced performance of the proposed control scheme with nonlinear parameter estimations under insufficient stability margins and validate its robustness in the event of actuator failures.     

仿尺蠖型火星车设计与验证

针对传统被动悬架式火星车在火星复杂地形运动中可能存在的问题，设计了仿尺蠖型火星车，包括沉陷脱困设计、爬坡设计、防托底设计和抬轮设计；识别了仿尺蠖型火星车移动系统的关键参数，计算得到不同火星车抬升高度的关键参数值和一个尺蠖运动周期的位移值；计算结果表明仿尺蠖型火星车可以解决被动悬架式火星车的上述问题。对所设计的仿尺蠖型火星车开展了地面试验和飞行试验验证，地面试验验证了车轮沉陷脱困能力、越障能力和爬坡能力；作为仿尺蠖型火星车应用的实例，“祝融号”火星车成功经历了飞行试验，它在火星表面的顺利运行表明仿尺蠖型火星车设计措施有效。     

一种基于学习的高超声速飞行器智能控制方法

针对吸气式高超声速飞行器的飞行控制问题，提出一种基于学习的智能控制方法。为便于控制器设计，将飞行器动力学模型划分为速度子系统和高度子系统：为解决速度子系统控制输入受限的问题，提出一种基于强化学习的智能控制方案；对于考虑有限通信资源的高度子系统跟踪控制问题，提出一种基于事件触发的确定学习控制方案。该方案包含离线学习训练和在线触发控制两个阶段。首先在本地离线学习训练阶段获取并存储系统的未知动态知识，随后利用所获取的经验知识设计基于事件触发机制的在线触发控制器。本文所提方案基于学习的思想将离线学习训练获取的智能体和经验知识应用于在线控制，使得所提方案能够快速计算控制指令且通信资源占用少。仿真结果说明了所提出方法的有效性。     

DFCluster: An efficient algorithm to mine maximal differential biclusters for single pilot operations task synthesis safety analysis

With the continuous advancement of the avionics system, crew members are correspondingly reduced, and Single Pilot Operations(SPO) has attracted widespread attention from scholars.To meet the flight requirements in SPO mode, it is necessary to further strengthen air-ground coordination system integration, but at the same time, there will be some safety issues caused by resource integration, function fusion, and task synthesis. Aimed at the safety problems caused by task synthesis, an efficient d...     

High dynamic output feedback robust control of cascaded extended state observer

High dynamic tracking performance is a key technical index of hydraulic flight motion simulator(HFMS). However, the strong nonlinearities, various model uncertainties and measurement noise in hydraulic actuation systems limit the high dynamic performance improvement. In this paper, the outer axis frame of a HFMS is taken as a case study and its nonlinear dynamic model with consideration of strong nonlinearities, matched and mismatched uncertainties is established.A novel cascaded extended state ...     

A geographical and operational deep graph convolutional approach for flight delay prediction

Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning, flight scheduling, airport operation, and passenger service. Flight delay is affected by numerous factors and irregularly propagates in air transportation networks owing to flight connectivity, which brings critical challenges to accurate flight delay prediction. In recent years, Graph Convolutional Networks (GCNs) have become popular in flight delay prediction due to the advantage in extracting complicated relationships. However, most of the existing GCN-based methods have failed to effectively capture the spatial–temporal information in flight delay prediction. In this paper, a Geographical and Operational Graph Convolutional Network (GOGCN) is proposed for multi-airport flight delay prediction. The GOGCN is a GCN-based spatial–temporal model that improves node feature representation ability with geographical and operational spatial–temporal interactions in a graph. Specifically, an operational aggregator is designed to extract global operational information based on the graph structure, while a geographical aggregator is developed to capture the similar nature among spatially close airports. Extensive experiments on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods with a satisfying accuracy improvement.