变姿态垂直机降无人机的设计

本文设计了一种通过改变飞行姿态来实现垂直起降的无人机,可在垂直起降姿态和飞行姿态之间相互切换,有效地解决了无人机在发射和回收过程中事故率较高的问题。     

半环形翼短距起降无人机设计

该文设计了一种多用途短距起降无人机,采用2个半环形卡斯特沟槽翼分置于机身两侧作为升力的主要来源,外接一段平直翼用于控制无人机的横滚并提供附加升力,尾翼采用双垂尾布局以增加稳定性,整机长度为12.30 dm,翼展为18.60 dm,机翼总面积为44 dm2,尾力臂为6.78 dm。具有短距离起降的特性,自动姿态修正、自动航线飞行、自主定速定高巡航、自动返航等功能,可用于农业、运输、军事、测绘等需要载重作业的领域,预期具有更广泛的应用。     

多用途扇翼机设计与实现

该文设计了一款多用途扇翼飞行器，在扇翼机的基础上对扇翼机的实际用途进行探究并实现相关功能。该文研究的对象是一架机长为970 mm、翼展为1 200 mm且翼面积为39.35 dm²的扇翼机。试验结果表明，该无人机具有超短距起降、大迎角不失速、操纵控制简单、低速飞行稳定性和安全性好等优点，是一种性能介于直升机和固定翼机之间的新型无人机，搭配现有设备就可以在军事、农业、救灾以及运输等方面具有广泛的应用价值。     

二轴倾转旋翼无人机的设计及实现

该文结合多旋翼无人机和固定翼无人机的优点设计了二轴倾转旋翼无人机,这是一款摒弃传统设计观念的新型无人机。采用碳纤维复合材料以及巴尔沙木材,在最大程度减重的同时又保证了机体的强度。通过机载STM32处理器引导输出不同宽度的PWM波脉控制舵机带动发动机吊舱实现倾转。在垂直飞行姿态下,其最大推重比可达2,在水平飞行姿态下,其续航时间为52 min。其凭借着大推重比和高续航时间,可在山区、灾区等复杂环境中进行勘察、巡航等作业。     

机翼可收缩式飞行汽车设计

该文针对飞行汽车中机翼可收缩这一概念,设计出一款可以实现垂直起降和收缩机翼的飞行汽车概念模型。通过CATIA、CAD、SolidWorks和Profili这4款软件分别对涵道风扇、车身结构、可收缩机翼进行建模分析。对飞行汽车的外形设计以及航行状态的各项基本参数进行了估算,分别叙述了可收缩机翼、涵道风扇、车身材料及外形设计的具体方案,以及不同部件的工作过程及其原理。通过论证得出,该飞行汽车的设计方案可以改变城市交通拥堵的现状,革新现有的交通方式,促进高新技术的发展。     

柔性气动外形飞行体气流扰动下的可视化检测

为研究具有柔性气动外形的微型飞行器抗气流扰动特性,本文设计了一种具有柔性气动外形微型旋翼飞行体,利用有限元方法对飞行体垂直气道的设计模型进行了模态分析,实现了飞行体的垂直起降和悬停。在基于双目立体视觉测量的气流扰动场实验装置中对飞行体完成初步的三维可视化检测,利用频闪成像方法记录气流扰动下飞行体的运动轨迹和气动外形的形态学变化,为研究柔性气动外形非定常气动特性以及飞行体抗扰动机理提供一种有效的可视化信息提取方法。     

基于有限时间观测器的VTOL飞行器跟踪控制

针对垂直起降(VTOL)飞行器的轨迹跟踪控制问题,本文提出了一种基于有限时间控制的输出反馈控制方案。采用系统分解技术将原系统解耦成2个子系统,将原系统的输出轨迹跟踪问题转化为2个子系统的镇定问题。对解耦后的系统,利用滑模控制方法设计了一种状态反馈控制律,所设计的控制器能够保证闭环系统有限时间稳定。考虑部分状态不可测量情况下的VTOL飞行器轨迹跟踪控制问题,提出了一个有限时间快速收敛观测器,基于有限时间快速收敛观测器提出了有限时间输出反馈控制律。仿真结果表明,所提出的控制方法具有良好的跟踪性能,能够实现飞行器对给定参考轨迹的快速、准确跟踪。     

基于自适应浸入与不变的VTOL飞行器跟踪控制

针对输入存在不确定干扰的垂直起降(VTOL)飞行器的跟踪控制问题,提出了一种基于自适应系统浸入与不变(II)的控制方案:对于不确定性输入干扰,采用自适应II方法对于扰进行实时估计补偿;为便于控制器设计,采用系统分解技术将原系统解耦成一个最小相位误差子系统和一个非最小相位子系统,将原系统的输出跟踪问题转换为两个误差子系统的镇定问题。基于自适应II估计律和滑模变结构控制方法分别设计了两个子系统的控制器,所设计的控制器能够保证两个闭环子系统指数稳定。仿真结果表明,上述控制方案在干扰估计精度和收敛速度方面明显优于传统自适应估计方法,能够实现飞行器对给定轨迹的准确跟踪,同时抑制输入干扰对系统性能的不良影响。     

空气压缩航空发动机

本发明使用涡轮增压空气压缩机在压力腔里产生高温、高压气体，再利用压缩气体通过喷嘴喷出产生的反作用推力使飞行器垂直升降、在空中悬浮和向前飞行。由于结构简单故可大幅降低喷气式航空发动机的生产成本；可通过改变压力腔里压缩气体的喷出方向让飞行器垂直升降；不需使用昂贵的航空煤油，     

微型飞行器数据集成的硬件实现

为了实现微型飞行器的自主飞行控制,需要将多种传感器的数据集成起来,这些传感器包括摄像系统、GPS、高度计、加速度计、风速计、陀螺等.本文兼顾微型飞行器体积小、重量轻的要求,使用LM331作为关键器件使数据易于处理,提高了数据处理的速度和控制的反应速度,并给出了具体的方案和处理结果,证明了方案的可行件和有效性.另外,在研究微型飞行器的动力特性时,利用LM331的F/V转换特性,设计并研制了微型动力测试仪.     

A stress-influence-function with adaptive strength feature approach for stress constrained continuum topology optimization via small-loop sequential strategy

Recently, the stress-influence-function (SIF) approach is presented for stress constrained continuum topology optimization. The SIF approach provides an alternative for continuum topology optimization with stress constraints. However, the SIF approach is not good at controlling the maximum stress of the elements compared to the conventional approach. In the study, the stress-influence-function with adaptive strength feature (SIF-ASF) approach via small-loop sequential strategy is proposed to achieve better control on the maximum elemental stress. First, the stress constrained continuum topology optimization formulation is given and the SIF approach is briefly introduced. Then the SIF-ASF approach is proposed for stress constrained continuum topology optimization, in which the strength feature in the stress influence function is adjusted in each iterative step of the optimization process. The adjoint-vector based sensitivity analysis to the design variables is further discussed. Three numerical examples are given to illustrate the applicability and validity of the proposed SIF-ASF approach. It is shown that the proposed SIF-ASF approach can achieve better control on the maximum elemental stress than the SIF approach. Moreover, the proposed SIF-ASF approach may obtain a lighter structure than the conventional approach.     

考虑混合约束的柔顺机构拓扑优化设计

为了满足制造工艺和静强度要求,提出一种综合考虑最小尺寸控制和应力约束的柔顺机构混合约束拓扑优化设计方法。采用改进的固体各向同性材料插值模型描述材料分布,利用多相映射方法同时控制实相和空相材料结构的最小尺寸,采用最大近似函数P范数求解机构的最大应力,以机构的输出位移最大化作为目标函数,综合考虑最小特征尺寸控制和应力约束建立柔顺机构混合约束拓扑优化数学模型,利用移动渐近算法求解柔顺机构混合约束拓扑优化问题。数值算例结果表明,混合约束拓扑优化获得的柔顺机构能够同时满足最小尺寸制造约束和静强度要求,机构的von Mises等效应力分布更加均匀。     

考虑混合约束的柔顺机构拓扑优化设计

为了满足制造工艺和静强度要求,提出一种综合考虑最小尺寸控制和应力约束的柔顺机构混合约束拓扑优化设计方法。采用改进的固体各向同性材料插值模型描述材料分布,利用多相映射方法同时控制实相和空相材料结构的最小尺寸,采用最大近似函数P范数求解机构的最大应力,以机构的输出位移最大化作为目标函数,综合考虑最小特征尺寸控制和应力约束建立柔顺机构混合约束拓扑优化数学模型,利用移动渐近算法求解柔顺机构混合约束拓扑优化问题。数值算例结果表明,混合约束拓扑优化获得的柔顺机构能够同时满足最小尺寸制造约束和静强度要求,机构的von Mises等效应力分布更加均匀。     

Constrained multi-objective optimization using constrained non-dominated sorting combined with an improved hybrid multi-objective evolutionary algorithm

Constrained multi-objective optimization problems (cMOPs) are complex because the optimizer should balance not only between exploration and exploitation, but also between feasibility and optimality. This article suggests a parameter-free constraint handling approach called constrained non-dominated sorting (CNS). In CNS, each solution in a population is assigned a constrained non-dominated rank based on its constraint violation degree and Pareto rank. An improved hybrid multi-objective optimization algorithm called cMOEA/H for solving cMOPs is proposed. Additionally, a dynamic resource allocation mechanism is adopted by cMOEA/H to spare more computational efforts for those relatively hard sub-problems. cMOEA/H is first compared with the baseline algorithm using an existing constraint handling mechanism, verifying the advantages of the proposed constraint handling mechanism. Then cMOEA/H is compared with some classic constrained multi-objective optimizers, experimental results indicating that cMOEA/H could be a competitive alternative for solving cMOPs. Finally, the characteristics of cMOEA/H are studied.     

Allocation of variance reduction targets under the influence of supplier interaction

A common quality improvement strategy used by manufacturers is to periodically allocate quality improvement targets among their suppliers. We propose a formal modelling and optimization approach for assessing quality improvement targets for suppliers. In this approach it is understood that a manufacturer's quality improvement results from reductions in supplier process variances, which occurs only through investments in learning. A constrained nonlinear optimization model is developed for determining an optimal allocation of variance reduction target that minimizes expected total cost, where the relationship between performance measures and the set of design parameters is generally represented by second-order polynomial functions. An example in the fabrication of a tyre tread compound is used both to demonstrate the implementation of our proposed models as well as to provide an empirical comparison of optimal learning rates for different functional relationships between the performance measures and the set of design parameters.     

基于工作流网的应急资源配置与路径规划集成优化

应急管理决策通常包括站点选址、资源配置、运输调度等内容,如何从应急处置整体流程控制的视角对决策内容进行集成建模及优化,是应急管理研究付诸实际应用的关键。本文提出具有资源和不确定时间约束的应急工作流网模型,通过三类库所（状态库所、动作库所、资源库所）及三类时间属性（可视时间、静态时间、动态时间）,揭示多部门联合应急中的作业时序与资源占用关系。在给定整体流程最大完成时间的条件下,以资源消耗与占用成本、资源运输与惩罚成本总和为目标函数,建立应急资源配置与路径规划的集成问题模型,并采用遗传粒子群混合算法对问题进行求解。根据遗传优化得到的应急资源配置方案,借助应急工作流网计算各动作库所、状态库所的时间参数,以此作为约束条件利用嵌套的粒子群算法进行资源运输策略优化。     

Solving constrained optimization problems with a hybrid particle swarm optimization algorithm

This article presents a particle swarm optimization algorithm for solving general constrained optimization problems. The proposed approach introduces different methods to update the particle's information, as well as the use of a double population and a special shake mechanism designed to avoid premature convergence. It also incorporates a simple constraint-handling technique. Twenty-four constrained optimization problems commonly adopted in the evolutionary optimization literature, as well as some structural optimization problems are adopted to validate the proposed approach. The results obtained by the proposed approach are compared with respect to those generated by algorithms representative of the state of the art in the area.     

Optimization approach in variable-charge potential for metal/metal oxide systems

Earlier attempts on minimizing the total system energy of metal/metal oxide systems with given charge constraints appear to be indirect and unnecessarily complicated. The energy minimization problem is in fact a constrained optimization problem and hence can be solved by a constrained optimization method. We propose a new direct approach for finding charge distributions among ions in molecular dynamics simulations. The approach is based on an optimization algorithm, called the Generalized Reduced Gradient (GRG) Method. This efficient approach can be readily employed in molecular dynamic simulations for metal/metal oxide systems.     

A modified version of a T‐Cell Algorithm for constrained optimization problems

In this paper, we present a heuristic inspired on the T‐Cell model of the immune system (i.e. an artificial immune system). The proposed approach (called T‐Cell) is used for solving constrained (numerical) optimization problems, and is validated using several test functions taken from the specialized literature on evolutionary optimization. Additionally, several engineering optimization problems are also used for assessing the performance of the proposed approach. The results are compared with respect to approaches representative of the state‐of‐the‐art in constrained evolutionary optimization. Copyright © 2010 John Wiley & Sons, Ltd.     

Multilevel intelligent scheduling and control system for an automated flow shop manufacturing environment

Current manufacturing scheduling and control systems are incapable of coping with complex system dynamics inherent in real-world situations and, hence, human intervention is required to maintain real-time adaptation and optimization. A unique feature of biological intelligent systems is that they build and improve over their communication, decision-making and control structures in real time autonomously. A challenge is now emerging in the design of manufacturing systems where on-line adaptation and optimization become increasingly important. This paper reports on the development of a new integrated intelligent scheduling and control system for an automated manufacturing environment using a multilevel approach. At the first level, a conventional scheduling and control system is considered, then at the second level, a new fuzzy logic mechanism is developed to enable the conventional system to improve and perceive the changes of system parameters adaptively. A new perturbation mechanism is embedded in the third level to implement on-line optimization for coping with the more complex structural changes of system dynamics. The final level is composed of artificial neural networks that can learn from experiences provided by the perturbation mechanism. The approach is designed to improve system intelligence gradually to cope with various forms of systems dynamics. A fully automated flow shop manufacturing system is taken to demonstrate this approach.