Neural network-based sliding mode control for atmospheric-actuated spacecraft formation using switching strategy

This paper presents an adaptive neural networks-based control method for spacecraft formation with coupled translational and rotational dynamics using only aerodynamic forces. It is assumed that each spacecraft is equipped with several large flat plates. A coupled orbit-attitude dynamic model is considered based on the specific configuration of atmospheric-based actuators. For this model, a neural network-based adaptive sliding mode controller is implemented, accounting for system uncertainties and external perturbations. To avoid invalidation of the neural networks destroying stability of the system, a switching control strategy is proposed which combines an adaptive neural networks controller dominating in its active region and an adaptive sliding mode controller outside the neural active region. An optimal process is developed to determine the control commands for the plates system. The stability of the closed-loop system is proved by a Lyapunov-based method. Comparative results through numerical simulations illustrate the effectiveness of executing attitude control while maintaining the relative motion, and higher control accuracy can be achieved by using the proposed neural-based switching control scheme than using only adaptive sliding mode controller.     

An LMI-based decoupling control for electromagnetic formation flight

Electromagnetic formation flight(EMFF) leverages electromagnetic force to control the relative position of satellites. EMFF offers a promising alternative to traditional propellant-based spacecraft flight formation. This novel strategy is very attractive since it does not consume fuel. Due to the highly coupled nonlinearity of electromagnetic force, it is difficult to individually design a controller for one satellite without considering others, which poses challenges to communications.This paper is devoted to decoupling control of EMFF, including regulations, constraints and controller design. A learning-based adaptive sliding mode decoupling controller is analyzed to illustrate the problem of existing results, and input rate saturation is introduced to guarantee the validity of frequency division technique. Through transformation, the imposed input rate saturation is converted to state and input constraints. A linear matrix inequalities(LMI)-based robust optimal control method can then be used and improved to solve the transformed problem. Simulation results are presented to demonstrate the effectiveness of the proposed decoupling control.     

Connectivity preservation and collision avoidance control for spacecraft formation flying in the presence of multiple obstacles

This paper addresses connectivity preservation and collision avoidance problem of spacecraft formation flying with multiple obstacles and parametric uncertainties under a proximity graph. In the proximity graph, each spacecraft can only get the states of the neighbor spacecraft within its sensing region. Connectivity preservation of a graph means that the connectivity of the graph should be preserved at all times during spacecraft formation flying. We consider two cases: (i) the obstacles are static, and (ii) the obstacles are dynamic. In the first case, a distributed continuous control algorithm based on artificial potential function and equivalent certainty principle is proposed to account for the unknown parameters and the static obstacles. In the second case, a sliding surface combined with a distributed adaptive control algorithm is proposed to tackle the influence of the dynamic obstacles and the unknown parameters at the same time. With the distributed control algorithms, the desired formation configuration can be achieved while the connectivity of the graph is preserved and the collisions between the spacecraft and the obstacles are avoided. Numerical simulations are presented to illustrate the theoretical results.     

舰载无人机撞网回收自适应制导技术

针对无人机自动着舰撞网回收过程中目标舰船处于运动状态的特点,借鉴导弹导引律的比例导引法提出了基于视线角的制导律,并引入反步法的设计思想以提高制导律的自适应性。基于视线角的制导律使无人机的轨迹倾斜角变化率与视线角变化率成比例,通过控制视线角来跟踪下滑轨迹倾斜角。采用该导引律可以减小无人机运动对目标舰船参数变化的敏感性,从而获得较为稳定的下滑轨迹。仿真结果表明了该制导律的可行性,并且该制导律具有较强的鲁棒性。     

舰船机舱内测温系统准确性试验与分析

为研究舰船机舱内测温系统的工作情况,对两型舰船的热电阻测温系统和热电偶测温系统进行了试验。分别在机舱环境内和实验室环境内对相同的测温系统进行了测试,结果表明热电阻测温系统受机舱环境影响较小,能够准确地监测温度;热电偶测温系统受机舱环境影响较大,测量结果在高温段产生了失真,本文分析了热电偶测温系统产生偏差的原因。     

舰艇编队网络化反导作战指挥体系研究

网络中心作战条件下,传统作战指挥体系已经无法满足作战指挥需要,文章从指挥体制和指挥方式两方面探讨了适应舰艇编队网络中心作战模式的新型指挥体系;基于网络中心作战模式下舰艇编队作战指挥特点,分析了传统指挥体制和指挥方式的不足,提出一种由动态网络型指挥体制和动态分权式指挥方式构成的新指挥体系,并分析了其在指挥可靠性、整体性和时效性方面的优势。     

基于零空间方法的四旋翼无人机避障与协同编队控制

针对四旋翼无人机在编队飞行执行任务时可能遭遇障碍物问题,考虑多无人机避障及机间避撞的需求,提出 1种基于零空间方法的四旋翼无人机避障与协同编队控制算法。首先,建立四旋翼无人机动力学模型,并建立虚拟控制量简化控制模型;其次,基于零空间方法进行避障与协同编队控制算法研究,将无人机任务执行分解为目标趋向任务、避障避撞任务和协同编队任务,并根据优先级进行任务融合得到期望速度;再次,基于 PID方法设计控制律;最后,通过仿真验证所提控制算法的有效性。所提方法可保证四旋翼无人机在编队飞行中遭遇障碍物时的飞行安全。     

基于双向最近邻准则的编队预定目标选择方法

相比传统的预定目标选择方法,基于编队形状的反舰导弹预定目标选择方法是一类性能优越的新方法。其原理是利用导弹飞行期间舰艇编队形状的相对稳定性,并将发射前装订的编队形状和末制导雷达探测到的编队形状表示为2个点集,通过点集匹配实现预定目标选择。提高这类算法性能的关键在于研究合理的点集距离函数。文章提出一种基于共同点的距离函数,研究了其抗干扰的机理,并证明其计算过程可通过双向最近邻准则实现。仿真试验表明该方法性能优于以往的MHD方法。     

某型登陆舰的舰面流场仿真与分析

舰载直升机是登陆舰实现"垂直登陆"的主要武器,研究舰面流场特性对直升机安全性能与操纵性都有重要意义。建立了某型登陆舰的三维模型,以风向角为30°,风速为15m/s的流场为例,通过数值仿真得到了飞行甲板上方的气流分布,经分析机库陡壁效应引起的下冲气流分量在机库后方形成了涡流区,在直升机的起降过程中应避免通过该涡流区,以保障直升机的安全起降、平稳飞行。     

基于多目标决策模型的两栖攻击舰编队编成研究

两栖攻击舰编队编成不能照搬固定的模式,而应根据作战任务和威胁等因素进行合理配置。文章分析了两栖攻击舰编队的作战能力,并在此基础上构建了编成模型的指标体系。运用多目标决策理论,建立了两栖攻击舰编队编成的模型,通过计算得到了针对某作战行动中两栖攻击舰编队的最优编成方案,并将所得到的结果与日常性建制的编队编成进行了比较。