基于HJI理论的无轴承异步电机悬浮系统滑模鲁棒控制

为了实现对无轴承异步电机悬浮系统动态解耦控制,提出一种基于HJI理论无轴承异步电机悬浮系统滑模鲁棒控制方法。在悬浮系统建模时,将系统不确定性以及外界扰动考虑其中,并通过设计合适的滑模控制律满足HJI不等式鲁棒条件来确保控制系统的稳定性;最终,该方法实现无轴承异步电机悬浮系统动态解耦控制并提高了系统的稳定性和抗扰动性能。仿真和试验结果证明了该方法的有效性,能够实现两自由度无轴承异步电机径向悬浮力之间解耦控制。     

考虑数值噪声的热-电耦合系统分析

由于仿真计算中数值噪声的影响,优化的设计函数常常是不光滑或不连续的,为多学科之间的解耦和优化计算带来较大困难。为此,借鉴全局优化的相关理论,提出了考虑数值噪声的热-电耦合系统分析方法。在解耦方法上,根据"同时分析和设计"(SAND)思想,将解耦问题转化为一个优化问题,在学科层引入Kriging替代模型以便过滤数值噪声,并采用极大似然估计法确定新增样本点的位置,较大程度上减少了解耦分析所需的重分析次数,通过一个典型的热-电耦合算例验证了模型和方法的有效性。     

基于SysML的多学科设计建模与优化

目的 基于系统建模语言SysML,分析多学科设计建模与优化过程,在理解多学科设计与优化数学模型的基础上,构建系统设计优化模型。方法 通过分析多学科设计优化的数学模型,利用SysML语言对多学科优化对象模型进行元模型表征,将生成的SysML模型进行模型转化,转换成XML格式以便优化求解器进行求解。结论 提出了一种用于多学科设计建模与优化的SysML扩展优化建模方法。通过SysML系统建模语言的扩展版型,添加多学科优化相关的优化目标、优化约束、优化变量等优化元素的模型内容。提出了SysML优化信息的提取方法,以XML为中间格式,将提取的优化模型与优化求解器进行集成。通过系统设计与系统优化的集成求解为产品系统架构设计人员提供有效的决策支撑。     

汽车发动机悬置系统多目标设计优化研究

动力总成悬置系统对于汽车振动与噪声控制十分重要,通过考虑车身耦合因素,建立动力总成悬置系统十五自由度耦合模型,以扭矩轴解耦率和总传递振动力为综合优化目标进行优化,并将整车常用行驶工况考虑在内,以整车实测数据辨识出发动机激振力作为系统实际输入,应用粒子群算法对悬置系统刚度参数进行优化。计算表明选择合适的刚度参数可以有效降低汽车的传递振动力,并提高扭矩轴解耦率,从而改善汽车乘坐的舒适性。     

某型电动客车悬置系统多目标优化

以某款电动客车动力总成悬置系统为研究对象,详细研究悬置系统的隔振性能评价指标,针对现有方法在优化过程中仅使用解耦率评价标准对悬置系统进行优化设计时造成的优化局限性,从而导致隔振效果较差的问题,提出一种能够兼顾悬置系统解耦率与传递率的优化策略。在MATLAB中建立6自由度动力学模型,在模型中建立涵盖解耦率和力传递率的优化目标函数,并明确仿真约束条件,利用NSGA-II多目标遗传算法对悬置刚度、安装位置及安装角度等参数进行优化计算。将经多目标优化后的结果与单独考虑解耦指标的优化结果进行对比分析,结果体现出多目标优化算法较普通优化法具有一定优越性。最后通过实车试验测试得到优化前后的隔振率试验指标。根据试验测试数据对比,结果显示出经多目标优化后的悬置系统隔振性能较高,能够较好地应用于悬置系统工程开发。     

汽车动力总成悬置系统鲁棒优化算法

应用鲁棒优化设计理论,考虑设计变量的不确定性对优化设计结果的影响,建立鲁棒优化模型。以动力总成悬置系统能量解耦为目标,悬置刚度参数为设计变量,考虑设计目标的均值和标准差,建立动力总成悬置系统的鲁棒优化模型。针对粒子群算法求解容易陷入局部最优解的问题,采用混合粒子群算法对动力总成悬置系统的悬置刚度参数进行鲁棒优化,并用Monte Carlo方法进行分析,以考察设计值的变化对目标函数的影响。结果表明,优化方法可以有效提高悬置系统的鲁棒性。     

气动弹性系统的模型确认与鲁棒颤振分析

研究了气动弹性系统的不确定性建模和鲁棒颤振分析问题.将结构的不确定性考虑为参数形式,非定常气动力的不确定性考虑为参数和未建模动态两种形式,建立了不确定系统的线性分式变换模型.分别使用基于Carathe-dory-Fejer插值定理和Nevanlinna-Pick插值定理的模型集检验方法进行了模型确认,在时间域和频率域中对模型集的有效性进行了验证,确定了不确定性的幅值.对于模型确认得到的不确定气动弹性系统,使用μ分析方法进行了鲁棒颤振分析.计算中,飞行速度是作为给定参数而不再是作为摄动变量,由此得到的鲁棒稳定性边界是匹配点解.仿真数值结果给出了鲁棒颤振速度,表明了方法的有效性.     

神经网络-模糊推理数据融合技术应用研究

近几年来,神经网络与模糊推理技术相辅相成构成了比较完备的智能信息系统框架.本文以神经网络-模糊推理数据融合技术为主线,重点介绍模糊神经多传感器数据融合系统的建模与分析;针对C3I数据融合系统中,传感器受外界复杂环境的影响使得其探测到的传感器信息具有不确定性,通过将模糊技术、神经网络理论与 Petri网相结合,讨论了模糊神经多传感器数据融合系统的建模方法,这对于提高系统学习能力和对外界环境的自适应能力具有实际意义.     

动力总成悬置系统频率和解耦率的稳健优化方法

由于测量误差、安装误差及老化等原因,动力总成悬置的刚度存在一定程度的误差或波动,从而悬置系统的频率和解耦率必然有一定程度的不确定性。考虑到通常容易得到悬置刚度的变化范围,在不需了解其统计特性的情况下,采用区间数描述悬置刚度、悬置系统的频率及解耦率的不确定性。给出了计算悬置系统频率和解耦率变化范围的改进区间截断方法,并验证了其计算精度。为提高悬置系统频率和解耦率的稳健性,提出一种区间型稳健优化方法（简称区间优化）对悬置刚度进行稳健设计。对某轿车悬置系统的频率和解耦率进行了稳健优化,结果表明,对于该悬置系统,稳健优化方法可以较大幅度地提高悬置系统侧倾和俯仰方向频率的稳健性,避免了悬置系统与其它零部件产生共振。与确定性优化相比,悬置系统在垂直方向和绕发动机曲轴扭转方向解耦率稍有降低,但能够满足悬置系统解耦布置的要求     

复杂非线性系统的H∞控制

本文针对含有不确定性,时延和未知系统状态的复杂非线性系统,考虑了输出反馈控制问题.应用模糊T-S模型逼近非线性系统建模,RBF神经网络作为补偿器来消除建模误差和不确定性.所设计的控制器能够使得闭环系统满足期望的H∞性能.     

复杂产品的多学科鲁棒协同设计优化方法研究

为了处理好复杂产品各子系统之间的耦合关系以及各子系统的异构性问题,以协同优化(CO)算法为基础,结合系统不确定分析(SUA)方法和近似不确定传播(IUP)方法,构建了多学科鲁棒协同设计优化算法框架.在设计变量的不确定性能够被概率分布函数描述的情况下,此算法框架能够解决复杂产品的设计优化问题.通过对梳齿式微加速度计的多学科鲁棒协同优化设计算例的计算,验证了此算法在输入参数存在微小扰动的情况下能够有效提高设计解的鲁棒性.     

Collaborative Reliability Analysis under the Framework of Multidisciplinary Systems Design

Traditional Multidisciplinary Design Optimization (MDO) generates deterministic optimal designs, which are frequently pushed to the limits of design constraint boundaries, leaving little or no room to accommodate uncertainties in system input, modeling, and simulation. As a result, the design solution obtained may be highly sensitive to the variations of system input which will lead to performance loss and the solution is often risky (high likelihood of undesired events). Reliability-based design is one of the alternative techniques for design under uncertainty. The natural method to perform reliability analysis in multidisciplinary systems is the all-in-one approach where the existing reliability analysis techniques are applied directly to the system-level multidisciplinary analysis. However, the all-in-one reliability analysis method requires a double loop procedure and therefore is generally very time consuming. To improve the efficiency of reliability analysis under the MDO framework, a collaborative reliability analysis method is proposed in this paper. The procedure of the traditional Most Probable Point (MPP) based reliability analysis method is combined with the collaborative disciplinary analyses to automatically satisfy the interdisciplinary consistency when conducting reliability analysis. As a result, only a single loop procedure is required and all the computations are conducted concurrently at the individual discipline-level. Compared with the existing reliability analysis methods in MDO, the proposed method is efficient and therefore provides a cheaper tool to evaluate design feasibility in MDO under uncertainty. Two examples are used for the purpose of verification.     

A decomposition‐based approach to uncertainty analysis of feed‐forward multicomponent systems

To support effective decision making, engineers should comprehend and manage various uncertainties throughout the design process. Unfortunately, in today's modern systems, uncertainty analysis can become cumbersome and computationally intractable for one individual or group to manage. This is particularly true for systems comprised of a large number of components. In many cases, these components may be developed by different groups and even run on different computational platforms. This paper proposes an approach for decomposing the uncertainty analysis task among the various components comprising a feed‐forward system and synthesizing the local uncertainty analyses into a system uncertainty analysis. Our proposed decomposition‐based multicomponent uncertainty analysis approach is shown to be provably convergent in distribution under certain conditions. The proposed method is illustrated on quantification of uncertainty for a multidisciplinary gas turbine system and is compared to a traditional system‐level Monte Carlo uncertainty analysis approach. Copyright © 2014 John Wiley & Sons, Ltd.     

Decoupled approach to multidisciplinary design optimization under uncertainty

We propose solution methods for multidisciplinary design optimization (MDO) under uncertainty. This is a class of stochastic optimization problems that engineers are often faced with in a realistic design process of complex systems. Our approach integrates solution methods for reliability-based design optimization (RBDO) with solution methods for deterministic MDO problems. The integration is enabled by the use of a deterministic equivalent formulation and the first order Taylor’s approximation in these RBDO methods. We discuss three specific combinations: the RBDO methods with the multidisciplinary feasibility method, the all-at-once method, and the individual disciplinary feasibility method. Numerical examples are provided to demonstrate the procedure. Anukal Chiralaksanakul is currently a full-time lecturer in the Graduate School of Business Administration at National Institute of Development Administration (NIDA), Bangkok, Thailand.     

Horsetail matching: a flexible approach to optimization under uncertainty*

It is important to design engineering systems to be robust with respect to uncertainties in the design process. Often, this is done by considering statistical moments, but over-reliance on statistical moments when formulating a robust optimization can produce designs that are stochastically dominated by other feasible designs. This article instead proposes a formulation for optimization under uncertainty that minimizes the difference between a design's cumulative distribution function and a target. A standard target is proposed that produces stochastically non-dominated designs, but the formulation also offers enough flexibility to recover existing approaches for robust optimization. A numerical implementation is developed that employs kernels to give a differentiable objective function. The method is applied to algebraic test problems and a robust transonic airfoil design problem where it is compared to multi-objective, weighted-sum and density matching approaches to robust optimization; several advantages over these existing methods are demonstrated.     

Gradient projection for reliability-based design optimization using evidence theory

Uncertainty quantification and risk assessment in the optimal design of structural systems has always been a critical consideration for engineers. When new technologies are developed or implemented and budgets are limited for full-scale testing, the result is insufficient datasets for construction of probability distributions. Making assumptions about these probability distributions can potentially introduce more uncertainty to the system than it quantifies. Evidence theory represents a method to handle epistemic uncertainty that represents a lack of knowledge or information in the numerical optimization process. Therefore, it is a natural tool to use for uncertainty quantification and risk assessment especially in the optimization design cycle for future aerospace structures where new technologies are being applied. For evidence theory to be recognized as a useful tool, it must be efficiently applied in a robust design optimization scheme. This article demonstrates a new method for projecting the reliability gradient, based on the measures of belief and plausibility, without gathering any excess information other than what is required to determine these measures. This represents a huge saving in computational time over other methods available in the current literature. The technique developed in this article is demonstrated with three optimization examples.     

复杂系统的设计认知和创新

目的研究复杂系统设计过程中设计师的设计认知和设计创新行为。方法通过国内外相关文献的研究和分析,总结归纳复杂系统设计问题界定和解决方案构思的认知过程、复杂系统创新设计构思的生成机制、复杂系统的创新设计策略,分析复杂系统设计认知和创新研究领域将发生的变革和未来发展的趋势。结论系统探讨了复杂系统设计认知与创新过程中的设计问题界定和解决方案构思的联合演化机制、给定和自发性解决方案示例,给设计师带来的认知固化和类比推理创新启发作用、结构化和机会主义的创新设计策略,发现当下的复杂系统创新设计理论和实践研究,还需要进一步开展跨学科知识融合激励的复杂系统创新设计研究,与此同时,加强关于设计师主观认知不确定性的定量建模研究,从而更好、更有针对性地探究复杂系统创新设计中认知过程的自然本质。     

Multifidelity approaches for optimization under uncertainty

It is important to design robust and reliable systems by accounting for uncertainty and variability in the design process. However, performing optimization in this setting can be computationally expensive, requiring many evaluations of the numerical model to compute statistics of the system performance at every optimization iteration. This paper proposes a multifidelity approach to optimization under uncertainty that makes use of inexpensive, low‐fidelity models to provide approximate information about the expensive, high‐fidelity model. The multifidelity estimator is developed based on the control variate method to reduce the computational cost of achieving a specified mean square error in the statistic estimate. The method optimally allocates the computational load between the two models based on their relative evaluation cost and the strength of the correlation between them. This paper also develops an information reuse estimator that exploits the autocorrelation structure of the high‐fidelity model in the design space to reduce the cost of repeatedly estimating statistics during the course of optimization. Finally, a combined estimator incorporates the features of both the multifidelity estimator and the information reuse estimator. The methods demonstrate 90% computational savings in an acoustic horn robust optimization example and practical design turnaround time in a robust wing optimization problem. Copyright © 2014 John Wiley & Sons, Ltd.     

复杂系统的多学科设计优化综述

从设计和分析的本质出发,结合复杂系统的特点,通过分析传统设计优化流程在面对复杂系统时存在的困难和缺陷,指出多学科设计优化(multidisciplinary design optimization,MDO)方法是解决复杂系统设计优化问题的一种有效措施.在此基础上,介绍了多学科优化方法的基本思想,总结了子系统耦合方式及MDO在处理耦合时的基本方法,归纳了MDO的知识框架和主要研究内容.最后在现有研究成果的基础上,对MDO今后的研究提出了几点参考意见.