Fuzzy Q learning algorithm for dual-aircraft path planning to cooperatively detect targets by passive radars

The problem of passive detection discussed in this paper involves searching and locating an aerial emitter by dualaircraft using passive radars. In order to improve the detection probability and accuracy, a fuzzy Q learning algorithrn for dual-aircraft flight path planning is proposed. The passive detection task model of the dual-aircraft is set up based on the partition of the target active radar＇s radiation area. The problem is formulated as a Markov decision process （MDP） by using the fuzzy theory to make a generalization of the state space and defining the transition functions, action space and reward function properly. Details of the path planning algorithm are presented. Simulation results indicate that the algorithm can provide adaptive strategies for dual-aircraft to control their flight paths to detect a non-maneuvering or maneu- vering target.     

Sensor planning method for visual tracking in 3D camera networks

Most sensors or cameras discussed in the sensor network community are usually 3D homogeneous, even though their2 D coverage areas in the ground plane are heterogeneous. Meanwhile, observed objects of camera networks are usually simplified as 2D points in previous literature. However in actual application scenes, not only cameras are always heterogeneous with different height and action radiuses, but also the observed objects are with 3D features（i.e., height）. This paper presents a sensor planning formulation addressing the efficiency enhancement of visual tracking in 3D heterogeneous camera networks that track and detect people traversing a region. The problem of sensor planning consists of three issues：（i） how to model the 3D heterogeneous cameras;（ii） how to rank the visibility, which ensures that the object of interest is visible in a camera＇s field of view;（iii） how to reconfigure the 3D viewing orientations of the cameras. This paper studies the geometric properties of 3D heterogeneous camera networks and addresses an evaluation formulation to rank the visibility of observed objects. Then a sensor planning method is proposed to improve the efficiency of visual tracking. Finally, the numerical results show that the proposed method can improve the tracking performance of the system compared to the conventional strategies.     

Improved algorithms to plan missions for agile earth observation satellites

This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.     

基于遗传算法的RLV再入轨迹优化设计

飞行器再入轨迹优化是一类最优控制问题。传统的优化方法存在初始值敏感问题。利用小生境技术和精英方法改进适应值共享拥挤遗传算法,并将其用于RLV再入轨迹优化设计。以终端时间固定的空间最小控制能量再入轨迹和终端时间自由的平面最小热载再入轨迹为例,详细讨论了遗传算法用于再入轨迹优化设计所需要解决的一些关键问题。仿真结果表明提出的方法能够较快地搜索到全局最优解,对初始猜测值不敏感,能够方便用于RLV的再入轨迹方案选择和优化设计。     

High-order sliding mode attitude controller design for reentry flight

A novel high-order sliding mode control strategy is proposed for the attitude control problem of reentry vehicles in the presence of parametric uncertainties and external disturbances, which results in the robust and accurate tracking of the aerodynamic angle commands with the finite time convergence. The proposed control strategy is developed on the basis of integral sliding mode philosophy, which combines conventional sliding mode control and a linear quadratic regulator over a finite time interval with a free-final-state and allows the finite-time establishment of a high-order sliding mode. Firstly, a second-order sliding mode attitude controller is designed in the proposed high-order siding mode control framework. Then, to address the control chattering problem, a virtual control is introduced in the control design and hence a third-order sliding mode attitude controller is developed, leading to the chattering reduction as well as the control accuracy improvement. Finally, simulation examples are given to illustrate the effectiveness of the theoretical results.     

再入机动飞行器最优轨迹设计与跟踪

提出了一种再入机动飞行器（maneuvering reentry vehicle, MRV）的最优制导与控制方案。针对MRV再入机动轨迹优化问题,提出了新的基于Gauss伪谱优化方法的分段优化策略;由产生的最优参考轨迹,生成系统的外环最优制导指令。接着利用轨迹线性化控制（trajectory linearization control, TLC）方法设计系统的内环控制律。基于MRV完整的非线性六自由度模型,仿真表明提出的再入轨迹优化设计方法是有效的,而且内环TLC控制器可以准确地跟踪外环的最优制导指令和最优轨迹,并对系统未建模特性和参数不确定性具有一定的鲁棒性能。     

利用高斯伪谱法求解具有最大横程的再入轨迹

为了使升力式飞行器再入大气层后取得最大横程,采用高斯伪谱方法求解最优再入轨迹。利用微分形式高斯伪谱方法将飞行器三自由度再入轨迹优化问题转化为非线性规划问题,选取高斯节点上的状态量和控制量作为待优化参数,并将最优性能指标选为横程最大,然后对再入轨迹进行了求解。通过与按最大升阻比飞行方案所得结果进行对比,表明按所提方法求取的再入轨迹优于后者。此外,仿真过程还说明高斯伪谱法对状态猜测值并不敏感,算法容易收敛,适用于轨迹优化问题的求解。     

Improved insensitive to input parameters trajectory clustering algorithm

The existing trajectory clustering （TRACLUS） is sensitive to the input parameters c and MinLns. The parameter value is changed a little, but cluster results are entirely different. Aiming at this vulnerability, a shielding parameters sensitivity trajectory cluster （SPSTC） algorithm is proposed which is insensitive to the input parameters. Firstly, some definitions about the core distance and reachable distance of line segment are presented, and then the algorithm generates cluster sorting according to the core dis- tance and reachable distance. Secondly, the reachable plots of line segment sets are constructed according to the cluster sorting and reachable distance. Thirdly, a parameterized sequence is extracted according to the reachable plot, and then the final trajectory cluster based on the parameterized sequence is acquired. The parameterized sequence represents the inner cluster structure of trajectory data. Experiments on real data sets and test data sets show that the SPSTC algorithm effectively reduces the sensitivity to the input parameters, meanwhile it can obtain the better quality of the trajectory cluster.     

再入飞行器深度确定性策略梯度制导方法研究

为解决传统再入飞行器轨迹制导方法对强扰动条件适应性不足,难以满足终端约束的问题,在深度确定性策略梯度学习框架基础上,通过对随机强扰动条件下的离线飞行轨迹进行网络训练,寻找不同环境影响条件下的最优动作网络,以用于在线干扰条件下的制导轨迹规划,可通过对再入飞行攻角和倾侧角剖面的周期性预测,满足再入飞行终端高度、航程和速度约束。仿真实验结果表明：在满足终端高度约束的条件下,最大终端剩余航程偏差小于500 m,最大终端速度偏差小于35 m/s。本文所提制导方法较传统跟踪制导方法有较大的精度提升,算法计算量小,具有较好的工程应用前景。     

高可靠性再入轨迹快速规划方法

提出一种高可靠性再入参考轨迹快速规划方法。在参考轨迹生成过程中,如果飞行路径角变化率不足够小,拟平衡滑翔条件(quasi equilibrium glide condition, QEGC)的应用前提得不到保证,利用QEGC所得的倾斜角剖面不保守,从而导致生成的参考轨迹有穿越再入走廊的风险。为得到高可靠性再入轨迹,在倾斜角搜索过程中,除了使再入参考轨迹满足路径约束与终端约束外,同时将QEGC的应用前提“飞行路径角及其变化率为小量”作为倾斜角搜索的一项约束。计算结果表明所提方法能够保证再入轨迹的可靠性,同时基于QEGC规划方法的适应性得到保留。     

基于PCE方法的无人机轨迹规划与分析

针对无人机轨迹规划过程中不确定因素的特点,提出了一种利用多项式混沌展开(polynomial chaosexpansion,PCE),并获得基于人工势能法的模型最优参数的无人机轨迹规划方法。通过建立PCE代理模型,利用随机配置点法进行快速求解,规避了对已有的最优控制模型直接优化带来的计算资源不足的问题;通过Sobol灵敏度分析,减少不确定性参数在轨迹规划模型中的计算量;通过无人机轨迹规划案例,证明了方法的有效性。仿真结果表明:该方法与经典的人工势能模型相比,增加了不确定性分析,提高了模型计算效率。     

基于控制参数估计的再入滑翔目标智能轨迹预测算法

再入滑翔目标的轨迹预测是一项困难且具有意义的技术, 现有利用简单函数拟合控制参数进行轨迹预测的方法, 拟合精度不高且对数据的关联性不强。针对该问题, 本文结合长短期时序网络提出了基于控制参数估计的智能轨迹预测算法。首先, 通过设计快速轨迹生成算法, 结合攻角走廊模型快速生成大量机动轨迹, 构建数据集。然后, 建立了包含末点修正网络、控制参数修正网络及预测网络的智能轨迹预测框架, 利用数据集对关键控制参数的变化规律进行学习。最后, 结合目标运动模型积分外推实现轨迹的准确预测。仿真结果表明, 所设计的预测算法在不同机动模式下的预测平均误差不超过1.4 km, 最大误差不超过2.5 km, 能够实现轨迹的快速预测, 且对大气扰动造成的模型不确定性具有一定的鲁棒性。     

机器人系统计算机辅助示教仿真平台研究

首先提出了机器人计算机辅助试教系统的基本组成，接着探讨了Polyline型和Spline型两种轨迹的轨迹规划方法，并进一步讨论了末端件空间位姿矩阵的计算方法；给出了机器人关节运动量计算模型；最后列出了一个应用实例。     

多UCAV航迹规划研究

给出了一个实时、可行的多UCAV航迹规划和控制算法.构建了多UCAV航迹规划的系统总体结构;提出以协同时间作为指标建立协同管理层的思想,并给出了多UCAV协同飞行时间的确定方法;基于voronoi图的方法规划了UCAV航路点路径;利用开关控制法进行了航迹控制.用Matlab仿真验证了算法的有效性.     

气象保障中基于遗传算法的航迹规划仿真

飞行器在执行任务过程中,需要根据三维空间和时间的气象信息变化、障碍物、威胁等信息以及飞行器自身的机动性能限制,计算出飞行航迹.主要通过基于遗传算法的航迹规划理论和应用MM5模式输出产品（风场、积冰、颠簸）信息,进行航迹路径的规划仿真验证与分析,给出了具有实际工程应用价值的飞行航迹.     

基于MOEA/D-ARMS的无人机在线航迹规划

无人机(unmanned aerial vehicle, UAV)在线航迹规划是UAV协同控制关键技术之一, 在线航迹规划问题本质上是一种动态多目标优化问题。为了求解该问题, 提出了一种基于自适应应答机制选择的动态多目标进化算法(multi-objective evolutionary algorithon based on decomposition-adaptive reaction mechanism selection, MOEA/D-ARMS)。多种应答机制构成应答机制池, 以应答机制最近一次的整体表现赋予应答机制一定的奖励, 并采用基于概率的方法从应答机制池中选择应答机制。MOEA/D-ARMS分别在静态环境情况、突发威胁情况、突变威胁情况和偏好改变情况下进行仿真实验。仿真结果表明, MOEA/D-ARMS可有效求解UAV在线航迹规划问题。     

切向加速度与弦误差约束的CNC系统时间最优轨迹规划

针对计算机数控(CNC)系统给定参数化路径, 给出了一种求解时间最优轨迹规划问题的凸优化方法. 轨迹规划问题考虑切向加速度约束与弦误差约束. 通过建立两种约束下的状态容许空间, 分析约束对时间最优轨迹的影响. 通过非线性变量代换, 时间最优轨迹规划问题被表述为一个与时间无关的凸最优控制问题. 基于控制向量参数化(CVP)方法, 问题被进一步转化为易于求解的凸优化问题. 以路径参数对时间的二阶导数(参数加速度)为优化变量, 序列二次规划(SQP)方法获得问题数值解. 文末通过求解两个测试路径的时间最优轨迹规划问题, 验证方法的有效性.     

航线天气预报中航迹规划仿真研究

在实际的航线天气预报中,应该根据三维空间和时间的气象信息变化、障碍物、威胁等信息以及飞行器自身的机动性能限制,计算出飞行航迹。这里主要通过基于进化算法的动态路径规划思想,应用MM5模式输出产品(风场、积冰和颠簸)信息,进行航迹路径的规划仿真验证与分析,给出了具有实际应用价值的预报飞行航迹。     

基于混沌粒子群优化算法的翼伞系统轨迹规划

研究灾难环境下翼伞空投机器人系统轨迹规划问题,基于简化的翼伞系统质点模型,采用混沌粒子群优化算法对翼伞系统归航轨迹进行寻优。该方法采用非均匀B样条技术实现最优控制规律的参数化,将翼伞系统轨迹规划的最优控制问题转换成参数优化问题,进而运用混沌粒子群优化算法进行寻优计算。轨迹规划的控制曲线是光滑的,利于电机对翼伞系统的操纵伞绳实施控制。仿真结果表明,该方法对翼伞系统的轨迹规划控制是有效的。     

基于3DSAS的多约束多航迹协同规划与搜索方法

为解决多航迹规划中的避撞与协同问题,提出一种三维环境下的基于稀疏A*的多航迹协同规划方法。该方法设计了一种规划框架,分别在航迹搜索前与搜索过程中考虑多航迹的协同问题。在航迹搜索过程中,不但结合单条航迹原有的约束条件,同时还进行了航迹间的碰撞检测,并且通过航迹的协同违背量等因素来进行启发式搜索。该方法可以有效处理各类约束条件,简化启发因子,从而获得更好的规划性能。