变信赖域序列凸规划RLV再入轨迹在线重构

针对可重复使用运载器（RLV）的再入轨迹重构问题,提出一种基于变信赖域序列凸规划的RLV再入轨迹快速求解方法. 首先,通过离散化及对非凸约束的线性化处理,将RLV的非凸轨迹优化问题转换为凸优化问题,然后通过序列凸规划方法对凸优化问题进行求解. 在序列凸规划求解过程的初始迭代中,采用预测校正算法对初值猜测轨迹进行设计,确定轨迹求解的终端时间;在后续迭代过程中,设计基于优化性能指标的信赖域更新策略,提升算法的收敛性能. 在轨迹快速求解方法的基础上,考虑RLV再入过程中可能发生的突发事件,如实际轨迹大幅度偏离参考轨迹或目标点变更,基于变化的初值约束及终端约束在线重构轨迹,并结合重构轨迹和LQR（Linear quadratic regulator）方法设计再入制导律实现对重构轨迹的有效跟踪. 最后,将此设计方法与Gauss伪谱法及传统序列凸规划算法进行仿真对比验证. 仿真结果表明:变信赖域序列凸规划方法相较于伪谱法和传统的序列凸规划方法在轨迹求解实时性及收敛性方面有较大的提升,具备应用于轨迹在线重构的能力,此外,所提出的轨迹在线重构方法具备良好的鲁棒性以及抗扰性.     

An enhanced hybrid and adaptive meta-model based global optimization algorithm for engineering optimization problems

Due to the good balance between high efficiency and accuracy, meta-model based optimization algorithm is an important global optimization category and has been widely applied. To better solve the highly nonlinear and computation intensive engineering optimization problems, an enhanced hybrid and adaptive meta-model based global optimization (E-HAM) is first proposed in this work. Important region update method (IRU) and different sampling size strategies are proposed in the optimization method to enhance the performance. By applying self-moving and scaling strategy, the important region will be updated adaptively according to the search results to improve the resulting precision and convergence rate. Rough sampling strategy and intensive sampling strategy are applied at different stages of the optimization to improve the search efficiently and avoid results prematurely gathering in a small design space. The effectiveness of the new optimization algorithm is verified by comparing to six optimization methods with different variables bench mark optimization problems. The E-HAM optimization method is then applied to optimize the design parameters of the practical negative Poisson’s ratio (NPR) crash box in this work. The results indicate that the proposed E-HAM has high accuracy and efficiency in optimizing the computation intensive problems and can be widely used in engineering industry.     

基于重点域的自适应加点方法

代理模型已经普遍应用于复杂电子装备的设计中,以提高计算效率、缩短产品的设计周期。该文在重点域的基础上,提出了一种自适应加点方法。该方法每次进行迭代时,通过建立局部加点模型和全局加点模型选取更新点,以分别提高代理模型的局部开发能力和总体探索能力,进而提高代理模型的整体优化效率和预测能力。通过几个标准数学测试函数,验证了该方法的有效性。最后,将该方法应用于某共形天线的优化设计实例中,得到了满意的结果。     

超冗余移动机械臂的逆运动学快速求解

为了快速并精确地求解一类冗余移动机械臂(MM)运动学的优化逆解,提出了一种基于遗传信
赖域算法和解析解法相结合的运动学求逆方法. 首先使用解析解法求出机械臂逆解关于移动
车运动参数的表达式,据此表达式引入关节最佳柔顺性准则确定信赖域算法的目标函数;然
后用遗传信赖域算法快速求解此只包含移动车运动参数为自变量的目标函数的最优化问题,
据此求出运动学逆解. 仿真算例表明,该方法能快速求出精确和优化的运动学逆解.     

解非线性互补问题带线搜索的非单调自适应信赖域法

基于Fischer-Burmeister(FB)函数将非线性互补问题等价地转化为求解无约束优化问题。结合自适应信赖域半径方法和基于函数平均权重的非单调技术,提出一个求解非线性互补问题的非单调自适应信赖域方法。在适当的假设条件下,证明了该算法的全局性和超线性,数值结果表明该算法是可行的。     

改进人工势场法的移动机器人路径规划

针对传统人工势场法应用于移动机器人路径规划存在的缺陷,建立了改进的人工势场模型:使用势场强度代替力矢量进行路径规划;在障碍物的斥力势场中添加系数项,解决障碍物与目标点过近导致的目标不可达问题;考虑移动障碍物速度与机器人速度的影响,将速度信息引入到势场函数中;引入"填平势场"引导机器人走出局部极小点.在改进人工势场模型基...     

Fuzzy controller based on chaos optimal design and its application

In order to overcome difficulty of tuning parameters of fuzzy controller, a chaos optimal design method based on annealing strategy is proposed. First, apply the chaotic variables to search for parameters of fuzzy controller, and transform the optimal variables into chaotic variables by carrier-wave method. Making use of the intrinsic stochastic property and ergodicity of chaos movement to escape from the local minimum and direct optimization searching within global range, an approximate global optimal solution is obtained. Then, the chaos local searching and optimization based on annealing strategy are cited, the parameters are optimized again within the limits of the approximate global optimal solution, the optimization is realized by means of combination of global and partial chaos searching, which can converge quickly to global optimal value. Finally, the third order system and discrete nonlinear system are simulated and compared with traditional method of fuzzy control. The results show that the new chaos optimal design method is superior to fuzzy control method, and that the control results are of high precision, with no overshoot and fast response.     

支持向量机替代模型的遗传优化设计

针对实际工程中常见的性能函数不能显式表示的优化问题,提出一种基于支持向量机替代模型的遗传优化设计方法。利用试验设计选取合适的设计参数样本点,通过实验或数值仿真获得响应输出,结合遗传算法构建具有参数优化功能的支持向量机替代模型;将支持向量机模型作为目标性能函数,结合其他约束条件完成优化模型的建立,并应用遗传算法进行优化,形成一套准确、高效、适应性强的优化方法。以典型电子装备功分器的结构尺寸优化为例,采用均匀试验设计和高频电磁场仿真软件HFSS获取替代模型训练的学习样本,建立功分器模型的幅度比、相位差和驻波3个响应面目标函数,并对该多目标优化问题进行遗传寻优。     

一般约束优化含等式约束问题的鲁棒信赖域算法

针对一般约束优化问题进行了研究.利用引入罚函数将一般约束问题转化为一个只含不等式约束的的参数规划问题的技巧,将不等式约束优化问题的一个鲁棒信赖域算法扩展到一般约束优化问题中,并保留了算法的良好性质;同时,在一定条件下,得到了算法的全局收敛和超线性收敛.     

面向航程能力的固体火箭发动机方案设计优化

为解决飞行器航程最优为目标的固体火箭发动机方案设计优化问题,在固定规模约束下建立了复杂三维发动机装药、壳体和喷管的几何参数化模型及推力、质量的性能模型,同时,根据助推滑翔飞行器飞行环境严酷、性能要求高等特点,建立了多约束飞行器航程能力评估模型,然后采用改进自适应Legendre-Gauss-Radau伪谱法评估当前发动机方案对应的飞行器最大航程,构建了含约束的混合整数参数优化问题。针对高耗时混合整数参数优化问题,引入Kriging代理模型,对实数型序列近似优化算法进行了调整,加入了整数约束。针对传统全局加点准则收敛速度慢的问题,提出了局部增强策略,并进行了数值验证。仿真结果表明：采用改进自适应伪谱法得到的飞行轨迹满足全部约束要求,相对于初始固体火箭发动机方案,优化后的发动机方案对应的最大航程提升了12.89%,改进算法的优化结果与遗传算法一致,但耗时函数的调用次数仅为后者的2%,且相比于传统序列近似优化算法,该算法具有一定的全局搜索能力,有效改善了收敛速度。     

一个新的线搜索信赖域方法

利用一个修正的BFGS公式,提出了结合线搜索技术的BFGS-信赖域方法,并在一定条件下证明了该方法的全局收敛性和超线性收敛性.     

一个新的线搜索信赖域方法

利用一个修正的BFGS公式，提出了结合线搜索技术的BFGS一信赖域方法，并在一定条件下证明了该方法的全局收敛性和超线性收敛性．     

考虑发电约束的输电断面最大传输能力

为了打破以往输电能力求解过程中发电机端电压维持不变的假设,提出了考虑发电约束求解最大输电能力(TTC)的新方法.根据大型风电场并网及同步发电机调速器和励磁系统等动态元件的运行限制,建立了计算输电断面最大输电能力的优化模型,并采用信赖域内点法进行序列迭代求解.在信赖域内,将非线性优化问题逼近为线性规划(LP)子问题,以构造的价值函数为依据调整信赖域半径.在New England 39节点算例系统中验证了模型和计算方法的有效性.实验结果表明,考虑发电约束的输电断面最大传输能力计算结果更接近系统的实际运行情况.     

高超声速滑翔飞行器再入轨迹优化

为提高高超声速滑翔飞行器再入轨迹优化问题求解速度和精度,提出了一种将改进的麻雀智能优化同参数化设计相结合的再入轨迹方法。首先,通过Tent混沌映射和精英反向种群方法初始化种群,利用黄金正弦策略进行种群的位置更新,并通过余弦策略减少侦察者数量,采用贪婪策略对种群的最优解进行选择和更新,在增强算法全局搜索能力的同时,不影响收敛速度。然后,将高超声速再入轨迹优化问题转化为攻角剖面和倾侧角剖面的参数化设计问题,将路径约束转化为阻力加速度再入飞行走廊,保证再入过程中始终满足路径约束,利用罚函数法处理终端约束,从而使得飞行器精确命中目标。最后,采用改进的麻雀智能优化算法对设计参数进行寻优,使得目标函数最优。仿真实验表明：本研究所提出的改进麻雀算法相较于原始麻雀算法、鲸鱼算法和粒子群算法收敛速度快,得到的高超声速滑翔飞行器再入轨迹精度有了进一步的提高;蒙特卡洛仿真实验说明,本研究所提出的高超声速滑翔飞行器再入轨迹优化算法具有一定的鲁棒性。     

一个改进的BFGS信赖域算法及收敛性分析

针对无约束最优化问题,将BFGS公式与信赖域算法有机结合,给出了Bk的一个新的修正公式,在此公式中引入了一个可调整的参数θ,并且在一定条件下证明了该算法的全局收敛性.     

新型开放式超导MRI主磁体结构优化设计

针对超导磁共振成像（MRI）装置对磁体结构开放性的设计要求,提出了一种开放式超导MRI磁体结构.为了实现高磁场强度、高磁场均匀度以及低成本等设计目标,对设计的磁体结构进行了多目标优化.运用正交试验（OD）法确定影响磁体性能指标的关键设计变量,在此基础上,提出一种结合表面响应法和遗传算法的自适应优化策略,找出全局最优解,完成了主磁体结构的优化设计.实验结果表明,设计的超导型MRI主磁体不仅提供了一定的开放成像空间,同时也满足成像区磁场强度及均匀度等方面的设计要求.     

多重威胁下的无人机自主避障航迹规划

无人机作为一种新兴的无人作战力量和不可或缺的民用设备,现已渐渐融入到国家安全和社会发展中的各个方面,航迹规划是保障无人机顺利完成既定任务的核心环节.为解决规划空间存在诸多静态和动态威胁的实时航迹规划问题,提出了一种基于滚动时域的无人机自主避障航迹规划方法.首先将航迹规划模型构建为单目标函数优化问题,根据无人机简化运动学模型和约束条件,采用滚动优化策略生成最优航迹序列;然后对最优航迹序列之间的航迹再一次采用滚动优化策略产生子序列,综合考虑威胁和飞行约束,利用负梯度下降法搜索航路点,采用遗传算法对子序列进行规划;最后经反复滚动迭代优化可得近似全局最优航迹,同时利用贝塞尔曲线对航迹进行处理,使其表征实际的飞行航迹.实验仿真结果表明:验证了模型的合理性和方法的有效性;具有良好的威胁规避能力并能规划出一条光滑航迹;与全局规划方法相比,该方法减少了收敛时间,实时性更强,能够快速、鲁棒地收敛到近似全局最优解.     

基于灰色粒子群算法的可靠性稳健优化设计

为提高车辆零部件的安全性和稳健性,应用可靠性稳健优化设计理论和多目标决策方法,将车辆前轴的可靠性稳健优化设计转化为多目标优化问题。运用灰色理论中的关联分析法,选取粒子群算法中的全局极值和个体极值,提出了适合可靠性稳健优化设计中多目标模型求解的灰色粒子群算法。与传统方法相比,该方法更能迅速准确地得到车辆前轴的可靠性稳健优化设计信息。     

Efficient aero-structural design optimization: Coupling based on reverse iteration of structural model

Traditional coupled multi-disciplinary design optimization based on computational fluid dynamics/computational structure dynamics(CFD/CSD)aims to optimize the jig shape of aircraft,and general multi-disciplinary design optimization methodology is adopted.No special consideration is given to the aircraft itself during the optimization.The main drawback of these methodologies is the huge expanse and the low efficiency.To solve this problem,we put forward to optimize the cruise shape directly based on the fact that the cruise shape can be transformed into jig shape,and a methodology named reverse iteration of structural model(RISM)is proposed to get the aero-structural performance of cruise shape.The main advantage of RISM is that the efficiency can be improved by at least four times compared with loosely-coupled aeroelastic analysis and it maintains almost the same fidelity of loosely-coupled aeroelastic analysis.An optimization framework based on RISM is proposed.The aerodynamic and structural performances can be optimized simultaneously in this framework,so it may lead to the true optimal solution.The aerodynamic performance was predicted by N-S solver in this paper.Test shows that RISM predicts the aerodynamic and structural performances very well.A wing-body configuration was optimized by the proposed optimization framework.The drag and weight of the aircraft are decreased after optimization,which shows the effectiveness of the proposed framework.     

Design optimization of transonic compressor stage using CFD and response surface model

In order to shorten the design period, the paper describes a new optimization strategy for computationally expensive design optimization of turbomachinery, combined with design of experiment (DOE), response surface models (RSM), genetic algorithm (GA) and a 3-D Navier-Stokes solver(Numeca Fine). Data points for response evaluations were selected by improved distributed hypercube sampling (IHS) and the 3-D Navier-Stokes analysis was carried out at these sample points. The quadratic response surface model was used to approximate the relationships between the design variables and flow parameters. To maximize the adiabatic efficiency, the genetic algorithm was applied to the response surface model to perform global optimization to achieve the optimum design of NASA Stage 35. An optimum leading edge line was found, which produced a new 3-D rotor blade combined with sweep and lean, and a new stator one with skew. It is concluded that the proposed strategy can provide a reliable method for design optimization of turbomachinery blades at reasonable computing cost.