海上多任务飞机与无人机协同反潜作战研究

海上多任务飞机将成为海军执行海上巡逻侦察和反潜作战的主力航空武器装备,海上多任务飞机与无人机联合编队进行协同反潜作战将是未来航空反潜的发展趋势和重要作战方式之一。介绍P-8A海上多任务飞机主要系统配置和作战能力,分析海上多任务飞机的作战使用以及海上多任务飞机与无人机协同作战的概念和模式,包括协同态势感知和协同攻潜作战,指出了未来海上多任务飞机与无人机协同作战涉及的主要关键技术。     

UAV navigation in high dynamic environments: A deep reinforcement learning approach

Unmanned Aerial Vehicle (UAV) navigation is aimed at guiding a UAV to the desired destinations along a collision-free and efficient path without human interventions, and it plays a crucial role in autonomous missions in harsh environments. The recently emerging Deep Reinforcement Learning (DRL) methods have shown promise for addressing the UAV navigation problem, but most of these methods cannot converge due to the massive amounts of interactive data when a UAV is navigating in high dynamic environments, where there are numerous obstacles moving fast. In this work, we propose an improved DRL-based method to tackle these fundamental limitations. To be specific, we develop a distributed DRL framework to decompose the UAV navigation task into two simpler sub-tasks, each of which is solved through the designed Long Short-Term Memory (LSTM) based DRL network by using only part of the interactive data. Furthermore, a clipped DRL loss function is proposed to closely stack the two sub-solutions into one integral for the UAV navigation problem. Extensive simulation results are provided to corroborate the superiority of the proposed method in terms of the convergence and effectiveness compared with those of the state-of-the-art DRL methods.     

An intelligent target detection method of UAV swarms based on improved KM algorithm

Complete and efficient detection of unknown targets is the most popular application of UAV swarms. Under most situations, targets have directional characteristics so that they can only be successfully detected within specific angles. In such cases, how to coordinate UAVs and allocate optimal paths for them to efficiently detect all the targets is the primary issue to be solved. In this paper, an intelligent target detection method is proposed for UAV swarms to achieve real-time detection requirements. First, a target-feature-information-based disintegration method is built up to divide the search space into a set of cubes. Theoretically, when the cubes are traversed, all the targets can be detected. Then, a Kuhn-Munkres (KM)-algorithm-based path planning method is proposed for UAVs to traverse the cubes. Finally, to further improve search efficiency, a 3D real-time probability map is established over the search space which estimates the possibility of detecting new targets at each point. This map is adopted to modify the weights in KM algorithm, thereby optimizing the UAVs’ paths during the search process. Simulation results show that with the proposed method, all targets, with detection angle limitations, can be found by UAVs. Moreover, by implementing the 3D probability map, the search efficiency is improved by 23.4%–78.1%.     

无人平台SLAM技术研究进展

作为一种定位与感知方法,同时定位与建图(SLAM)是实现无人平台导航制导与控制的关键技术。阐述了SLAM技术的发展历史、研究现状与应用场景,分析并介绍了SLAM原理及其算法实现。此外,总结了若干SLAM领域的研究热点,并对SLAM技术的发展趋势进行了展望。     

Robust adaptive compensation control for unmanned autonomous helicopter with input saturation and actuator faults

This paper studies a robust adaptive compensation Fault Tolerant Control(FTC) for the medium-scale Unmanned Autonomous Helicopter(UAH) in the presence of external disturbances,actuator faults and input saturation.To improve the disturbance rejection capacity of the UAH system in actuator healthy case, an adaptive control method is adopted to cope with the external disturbances and a nominal controller is proposed to stabilize the system.Meanwhile, compensation control inputs are designed to reduce the negative effects derived from actuator faults and input saturation.Based on the backstepping control and inner-outer loop control technologies, a robust adaptive FTC scheme is developed to guarantee the tracking errors convergence.Under the presented FTC controller, the uniform ultimate boundedness of all closed-loop signals is ensured via Lyapunov stability analysis.Simulation results demonstrate the effectiveness of the proposed control algorithm.     

均等通信时滞下多UAV协同编队控制

多无人机(UAV)系统编队控制中,时滞是无法回避的问题,研究时滞对多UAV编队形成和系统稳定性的影响,具有重要理论价值。重点研究均等通信时滞下多UAV协同编队控制问题,并获得系统的稳定性条件。首先,设计具有均等通信时滞的协同编队控制律,得到多UAV编队系统的闭环时滞状态方程;在恒定均等时滞下,考虑到系统模型不确定性,基于线性矩阵不等式(LMI)理论得到系统的时滞依赖稳定性条件;最后,进行仿真实验,结果表明多UAV编队系统是稳定的,期望的编队队形能够形成并保持。     

基于积分器初值的无人机飞行控制律平滑切换方法

针对无人机自主飞行过程中多组复杂控制律之间的切换问题,提出了基于积分器初值的控制律平滑切换方法。通过将复杂控制律的各个环节进行形式变换,拆分成由比例和积分组成的基本单元结构;然后以舵面平滑切换为目标,依次递推出复杂控制律中所有积分器的初值,实现不同控制律之间的平滑切换。仿真验证了复杂飞行控制律之间平滑切换方法的有效性。     

复杂不确定性下多无人机的抗扰时变编队控制

针对存在不确定非线性动态和外部时变干扰的多无人机系统的时变编队问题,提出了基于扩张状态观测器(ESO)的抗扰编队控制方法。首先建立了分布式ESO来估计多无人机系统的不确定性,基于ESO的输出提出了抗扰编队控制律,并提出一套算法来对控制律进行参数选定。然后,通过分析得到基于该控制律下,多无人机系统实现抗扰时变编队所需要的充要条件,并最终严格证明了在满足编队充要条件和基于提出的控制律下,多无人机系统可以稳定实现抗扰时变编队。最后仿真结果表明理论方法的有效性。     

变论域模糊控制在无人飞行器马赫数控制中的应用研究

针对无人飞行器的控制要求,提出了一种增量式变论域模糊智能控制算法。该算法具有调节裕度大、鲁棒性强的特点,适用于非线性控制系统,解决了无人飞行器巡航马赫数缓慢升高不能稳定跟踪目标值的问题。通过模拟飞行轨迹的半实物仿真试验,验证了模糊智能控制算法的控制效果,试验结果表明,通过采用模糊控制方法大大提高飞行马赫数控制的动态性能,满足无人飞行器控制系统设计要求,取得了较好的控制效果。      

翼身融合无人机外形优化设计研究

翼身融合布局无人机具有较好的升阻特性和隐身特性,拥有广泛的应用前景。为了提高无人机的续航能力,兼顾翼身融合无人机的气动特性和结构重量要求,选择无人机升阻比与全机面积作为优化目标,应用多目标优化方法研究翼身融合无人机的外形设计,提出一种针对翼身融合无人机的外形参数化优化设计方法,并进行实例验证。结果表明:外形优化可以提高无人机升阻比、减轻结构重量,从而获得合理的翼身融合无人机设计方案。