拦截弹道快速设计方法

针对传统优化算法在解决多约束条件下拦截弹道设计问题时迭代计算耗时过长的缺点,提出了一种快速拦截弹道设计方法.该方法基于人工神经网络的非线性拟合技术,先通过普通优化算法进行离线样本计算,对样本中目标位置和优化得到的飞行程序参数进行输入-输出映射拟合训练,从而实现在给定目标位置的情况下对最优飞行程序参数的快速计算,达到提高拦截弹道设计效率的目的.经过数值仿真验证,将该方法应用于拦截弹道设计,在保证良好命中精度的同时,设计所需的计算时间大大减少,从而提高了弹道设计的时效性.      

镜像铣削加工奇异区域刀具路径优化

在大型薄壁件镜像铣削加工中,由奇异点引起的旋转轴运动不连续会影响加工质量,降低加工效率。针对此问题,分析了镜像铣削加工奇异点存在的原因,提出了一种奇异区域内的刀具路径优化方法。首先,针对镜像铣削系统的铣削头和支撑头分别建立了其旋转轴运动学变换模型,推导旋转轴微分运动关系,据此分析奇异点存在的原因及其对加工质量和效率的影响,定义了机床行程内的奇异区域范围。分析结果表明在镜像铣削系统行程范围内,仅铣削头存在奇异区域,而支撑头不存在奇异区域。然后,在镜像铣削加工刀路约束条件下,基于刀路光顺性指标建立了刀路优化模型。通过在加工曲面参数域内对跨越铣削头奇异区域的刀路进行调整,使得优化后的刀路更加光顺,以提高加工精度和表面质量,减少奇异区域附近的加工时间。最后,通过镜像铣削加工实验验证所提方法的有效性。     

垂直起降运载火箭返回轨迹不确定性优化

针对垂直起降运载火箭一子级在返回着陆的过程中存在的参数不确定性,提出了一种基于非侵入式多项式混沌展开的序列优化和可靠度评估的返回轨迹不确定性优化方法。首先,建立了返回多飞行段轨迹在确定性条件下的优化模型。然后,为同时兼顾轨迹的鲁棒性和可靠性,建立了由鲁棒最优目标函数、基于可靠度的路径约束和鲁棒等式约束组成的不确定性返回轨迹优化模型。最后,基于非侵入式多项式混沌展开方法对鲁棒目标函数和等式约束进行量化处理,将原随机鲁棒优化问题转化为高维状态空间中的等价确定性优化问题;为提高路径约束的可靠度评估效率,基于非侵入式多项式混沌展开方法对最可能点法进行改进,进一步发展了序列优化和可靠度评估策略。数值仿真结果表明,所提出的不确定性优化方法具有较好的鲁棒性,可以满足工程可靠性指标要求,同时还具有较高的精度和计算效率。     

三维弹道有理Bezier曲线造型与优化方法

为有效克服传统弹道优化方法解的收敛性和最优性受搜索算法、初始猜测等影响大的问题,提出了基于有理Bezier曲线的三维弹道造型与优化计算方法。根据边界条件用光滑且只含少量造型变量的有理Bezier曲线形成待优化三维造型弹道,采用逆动力学方法计算造型弹道倾角、弹道角速度和法向加速度等,结合弹道其它动力学方程和约束计算造型弹道性能指标,通过对弹道造型变量寻优得到最优弹道。这种方法将无限维弹道优化问题转换为对很少造型变量的参数优化问题, 而且得到的最优弹道完全光滑可飞。与间接法相比,无需求解两点边值问题使用方便、鲁棒性强;与直接法相比,不需要对动力学方程离散化、寻优变量少、求解收敛性好,而且解的全局最优性和光滑性更高。仿真结果及与自适应伪谱法的比较校验了方法的实用性和有效性。     

采用改进非支配近邻免疫算法的低轨混合星座设计优化

针对低轨同构星座覆盖资源在纬度上分布不均匀的不足,提出采用低轨混合星座提升覆盖均匀性的设计方案,并推导了满足全球任意点平均每天覆盖一定次数的最小卫星规模估算公式。针对非支配近邻免疫算法（NNIA）约束处理方面的不足,提出基于约束支配的改进非支配近邻免疫算法（Modified NNIA）,并以此设计了一种低轨混合星座优化平台来优化带约束的星座设计问题。仿真结果表明,改进的NNIA算法在收敛速度和多样性上均优于非支配分层遗传算法（NSGA II）和多目标粒子群算法（MOPSO）,可大大提高星座设计的效率。同时优化结果也表明低轨混合星座可提高覆盖的均匀性和大部分区域的覆盖次数,进而减少特定覆盖要求所需的卫星数目。     

多目标融合的冗余空间机械臂碰前轨迹优化

面向在轨对接与装配任务,提出了多目标融合的冗余度空间机械臂碰前轨迹优化方法。通过分析任务特点,阐述了空间机械臂碰前的多重任务目标。针对由空间机械臂连续轨迹跟踪、碰撞姿态控制及碰撞冲量幅值优化三个目标组成的第一类任务,通过设置任务优先级融合多目标并采用基于主任务零空间的方法实现对碰前轨迹的优化;而对由空间机械臂点到点规划、碰撞姿态控制及碰撞冲量幅值优化三个目标组成的第二类任务,通过设置任务权重将多目标融合并采用遗传算法对碰前轨迹进行优化。以典型七自由度机械臂为例开展仿真实验,验证了提出的轨迹优化方法的有效性。     

Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm

An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.     

Simple method for performance evaluation of multistage rockets

Multistage rockets are commonly employed to place spacecraft and satellites in their operational orbits. Performance evaluation of multistage rockets is aimed at defining the maximum payload mass at orbit injection, for specified structural, propulsive, and aerodynamic data of the launch vehicle. This work proposes a simple method for a fast performance evaluation of multistage rockets. The technique at hand is based on three steps: (i) the flight-path angle at each stage separation is guessed, (ii) the spacecraft velocity is maximized at the first and second stage separation, and (iii) for the last stage the thrust direction is obtained through the particle swarm optimization technique, in conjunction with the use of the Euler–Lagrange equations and the Pontryagin minimum principle. The coast duration at the second stage separation is optimized as well. The method at hand is extremely simple and easy-to-implement, but nevertheless it proves to be capable of yielding near-optimal ascending trajectories for a multistage launch vehicle with realistic structural, propulsive, and aerodynamic characteristics. The solutions found with the technique under consideration can be employed either for a rapid evaluation of the multistage rocket performance or as guesses for more refined optimization algorithms.     

Optimal nodal flyby with near-Earth asteroids using electric sail

The aim of this paper is to quantify the performance of an Electric Solar Wind Sail for accomplishing flyby missions toward one of the two orbital nodes of a near-Earth asteroid. Assuming a simplified, two-dimensional mission scenario, a preliminary mission analysis has been conducted involving the whole known population of those asteroids at the beginning of the 2013 year. The analysis of each mission scenario has been performed within an optimal framework, by calculating the minimum-time trajectory required to reach each orbital node of the target asteroid. A considerable amount of simulation data have been collected, using the spacecraft characteristic acceleration as a parameter to quantify the Electric Solar Wind Sail propulsive performance. The minimum time trajectory exhibits a different structure, which may or may not include a solar wind assist maneuver, depending both on the Sun-node distance and the value of the spacecraft characteristic acceleration. Simulations show that over 60% of near-Earth asteroids can be reached with a total mission time less than 100 days, whereas the entire population can be reached in less than 10 months with a spacecraft characteristic acceleration of 1 mm/s².