应用保护映射理论的高超声速飞行器自适应控制律设计

针对高超声速飞行器包线范围广、参数变化大的控制需求,应用保护映射理论提出一种高超声速飞行器的自适应控制律设计方法。首先建立整个飞行包线内的线性变参数(LPV)模型,在参数变化边界点设计一个初始的控制结构和参数,然后基于保护映射理论分析初始控制结构使闭环系统稳定的参数范围,通过迭代自动获取整个包线内满足性能指标的控制参数,进而通过多项式拟合设计出高超声速飞行器自适应控制律。所提出的方法能够根据初始控制结构自动寻找一系列满足性能要求的控制器参数,并确定这些控制参数满足闭环系统稳定的设计范围。仿真结果表明,所设计的自适应控制律能够确保高超声速飞行器大包线的设计要求,实现闭环系统的鲁棒稳定。     

有翼再入飞行器的超/高超声速气动力工程方法

有翼再入飞行器气动力的工程计算方法尚有待进一步完善,计算精度有待进一步提高。文章根据有翼再入飞行器的气动布局特点,改进和发展了一套适于其气动布局的部件划分策略和压强计算选取准则。以此对航天飞机轨道器和类X-34飞行器的纵向气动力特性进行工程计算,并与其风洞试验和数值模拟结果进行对比分析。结果表明,文中的工程计算方法在马赫数3以上的超/高超声速范围内可准确预测有翼再入飞行器的升阻特性,其中升力系数和阻力系数误差小于10%;并可准确预测俯仰力矩特性随迎角改变的变化规律。与现有方法相比,文中的部件划分策略和压强计算选取准则在升阻特性精度相当情况下,可明显提高俯仰力矩的预测精度,并反映飞行器表面的压强分布特征。     

临近空间大气压力传感器现场校准装置研究

本文针对临近空间大气压力传感器现场校准需求,介绍了临近空间大气压力传感器现场校准装置工作原理, 进行理论分析与仿真,提出需突破的关键技术,给出了不确定度预估。     

吸气式高超声速飞行器鲁棒反演控制器设计

针对吸气式高超声速飞行器气动／推进／结构弹性耦合控制问题,提出了鲁棒反演控制器设计方法。采用反演和动态逆方法设计虚拟控制量和实际控制量,通过引入一阶低通滤波器来获取虚拟控制量的导数,解决了虚拟控制量求导复杂问题;为了增强控制器的鲁棒性,采用充分光滑投影算子对模型非匹配不确定项进行估计和补偿,同时避免了可能出现的参数漂移问题。仿真结果表明,该控制器对模型气动参数拟合误差、攻角和升降舵偏角摄动、气动弹性影响具有鲁棒性,对速度指令和高度指令具有很好的跟踪效果。     

基于OCP的实时自适应资源管理技术研究

无人飞行器的自主飞行是一个非常复杂的过程,未来的先进无人飞行器要求能够对外界的环境扰动和突发事件做出迅速的反应,这就对资源的分配与任务的调度有了更高的要求,传统的技术手段已经不能适应先进无人飞行器的要求,本文从先进无人飞行器对QoS(Quality of service,服务质量)的要求出发,着重介绍了一种基于全新的OCP(Open Control P latform,开放式控制平台)的实时自适应资源管理技术,在介绍了一种Anytim e任务的调度方案之后,给出了下一步工作的重点。     

Aerodynamic performance of owl-like airfoil undergoing bio-inspired flapping kinematics

Natural flyers have extraordinary flight skills and their prominent aerodynamic performance has attracted a lot of attention. However, the aerodynamic mechanism of birds' flapping wing kinematics still lacks in-depth understanding. In this paper, the aerodynamic performance of owl-like airfoil undergoing bio-inspired flapping kinematics extracted from a free-flying owl wing has been numerically investigated. The overset mesh technique is used to deal with the large range movements of flapping airfoils. The bio-inspired kinematics consist of plunging and pitching movement. A pure sinusoidal motion and a defined motion composed of plunging of sinusoidal motion and pitching of the bio-inspired kinematics are selected for comparison. The other two NACA airfoils are also selected to figure out the advantages of the owl-like airfoil. It is found that the cambered owl-like airfoil can enhance lift during the downstroke. The bio-inspired kinematics have an obvious advantage in lift generation with a presence of higher peak lift and positive lift over a wider proportion of the flapping cycle. Meanwhile, the bio-inspired motion is more economical for a lower power consumption compared with the sinusoidal motion. The sinusoidal flapping motion is better for thrust generation for a higher peak thrust value in both upstroke and downstroke, while the bio-inspired kinematics mainly generate thrust during the downstroke but produce more drag during the upstroke. The defined motion has similar lift performance with the bio-inspired kinematics, while it consumes more energy and generates less thrust. The unsteady flow field around airfoils is also analyzed to explain the corresponding phenomenon. The research in this paper is helpful to understand the flight mechanism of birds and to design a micro air vehicle with higher performance.     

面向协同standoff跟踪问题的无人机制导律

针对多无人机（UAV）协同standoff跟踪问题,提出了UAV的横侧向和纵向制导律。对参考点制导（RPG）进行改进,作为UAV的横侧向制导律。然后,采用一组非线性微分方程对UAV和目标相对距离的调节过程进行建模,在此基础上证明了改进RPG的渐近稳定性,并推导了RPG参数与系统性能的关系,为RPG参数的选取提供了依据。最后,给出了UAV的纵向制导律,并分析了其渐近稳定性。仿真结果表明,改进RPG的跟踪误差和时间乘以误差绝对值积分（ITAE）指标均优于Lyapunov向量场制导（LVFG）和模型预测控制（MPC）,故改进RPG具有更快的响应速度和更高的稳态精度。     

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.     

高超声速飞行器推进系统建模

为在高超声速飞行器设计初期快速地获得推力和推力矩,以满足控制相关分析和建模需要.提出一种推进系统建模方法,基于激波/膨胀波相交理论来建模与机身耦合的进气道模型;用有摩擦变截面加热管来描述双模态燃烧室;将内喷管建模成一维变截面摩擦管,采用动量定理估算推力,并通过曲线拟合得到推力的解析表达式.与CFD计算结果相比,该模型计算得到双模态冲压发动机入口气流马赫数和温度误差小于5%,压强误差小于10%;计算得到的推力随马赫数、燃油当量比和迎角的增大而增加,随高度增加而减小,单个状态平均计算时间小于0.5s.计算结果表明:该建模方法满足面向控制建模的效率和精度需求,有助于此类飞行器设计初期的动力学和控制相关的分析和设计.