汽车动力总成主动悬置系统分层控制策略研究

针对汽车动力总成主动悬置系统结构特点,考虑作动器动态特性对系统控制精度的影响,提出了一种分层控制方法。在对三自由度1/4车主动悬置系统分析的基础上,推导了悬置系统和电磁作动器控制电路的数学模型,采用分层控制策略对悬置部分和作动器电路部分设计了上、下层控制器。上层悬置控制器采用综合性能较好的LQR控制,并利用遗传算法对其性能指标权重系数进行优化;下层作动器电路部分采用简单实用的PID控制,并利用粒子群算法对其参数进行优化。最后,通过对所设计的主动悬置系统设置两种典型工况进行仿真验证。结果表明:相比于传统控制,按照分层控制策略设计的主动悬置系统能够针对汽车不同工况实施更精确的控制,并且具有较强的鲁棒性和力跟踪性。     

基于可控性柱面网壳结构主动控制作动器布置的优化研究

利用磁致伸缩材料的磁控特性制作的作动器可以对结构进行主动控制。首先分析了这种作动器的工作原理和设计方法,并通过实验对其进行了输出性能测试。接着在对作动器进行动力学建模的基础上,推导出整个柱面网壳结构的作动控制方程,同时基于作动效率,提出了不依赖于控制方法的位置优化准则,并且在综合考虑控制效果系数、硬件成本和系统复杂性等因素的基础上,初步确定了作动器的数量,然后采用遗传算法,对作动器的布置位置进行了优化。最后利用LQR主动控制算法,对一柱面网壳模型结构进行了主动控制分析。结果表明,通过优化布置的作动器能够有效地减小结构的动力反应,是一种较好的主动控制方法。此外,主动控制模拟结果也验证了应用遗传算法优化此类问题的优越性和可靠性。     

新型宽带动力吸振器优化设计*

基于导纳功率流理论建立了变截面阻尼复合梁式新型宽带吸振器吸振分析理论模型,以输入被控制结构净功率峰值最小为目标函数,运用混沌粒子群算法对吸振器参数进行了优化,并给出了吸振效果较好的参数分布范围。结合实验结果表明：通过混沌粒子群算法优化后的得到的变截面阻尼复合梁式新型宽带吸振器具有好的吸振效果。     

建筑结构振动分散控制系统的多目标优化研究

基于结构可控性指标、粒子群—差分进化多目标混合群(MOHO)算法及分散控制系统结构随机响应求解提出了一种分散控制系统多目标优化设计方法,对结构分散控制系统的作动器位置、数量、控制器参数和子系统划分进行优化。利用结构可控性指标对作动器最优布置楼层进行确定;利用多目标混合群优化算法对分散控制系统内各子系统作动器数量和控制器反馈增益进行优化;以随机地震激励下反映结构振动控制效果和控制策略优劣的双指标作为各子控制系统的优化目标函数。针对一12层框架结构,采用提出的分散控制系统多目标优化方法对多种分散控制方案进行有优化设计,并在实际地震动激励下,对优化结果进行时程验证分析,表明该优化方法是稳定、有效可行的,优化得到的分散控制系统较集中控制系统而言有更理想的控制效果。     

基于声辐射模态有源解耦控制的溢出机理研究

基于声辐射模态有源控制,通过作动器位置布置可以使得控制过程解耦,通过对比解耦和非解耦两种控制方式,并对其控制结果进行讨论。发现解耦控制所需控制能量较小并且相对控制稳定,比非解耦控制有明显优势。并且两者在低频段都能取得良好的控制效果,在频率较高时出现不同程度的控制溢出。在解耦控制的基础上,通过分析各阶声辐射模态辐射效率特点,作动器布置对称形式与声辐射模态对称形式的对应关系,揭示了溢出的机理     

建筑结构的鲁棒分散控制方法研究

传统集中式控制方法需同时采用系统所有测量信号,进而计算出所有作动器的控制力并发出信号进行振动控制,其控制系统复杂且可靠性较差。近年来出现的基于系统局部信息反馈的分散控制策略,设计简单及可靠性高,已经成为目前研究的热点之一。目前分散控制策略的作动器控制力仅采用建筑物的相邻两层测量信号进行控制,虽然能够控制住结构的响应,但信息过少,控制效果不好。为了达到更好的控制效果,在鲁棒控制策略的基础上,通过设置特殊矩阵的方式,提出了一种基于建筑物相邻四层信号的鲁棒分散控制方法。从动力仿真的结果来看,本文方法的控制效果比仅依靠相邻二层信号的控制效果好得多。     

利用粒子群优化算法实现阻尼比和频率的精确识别

摘要：本文提出了一种利用粒子群优化算法辨识阻尼比和频率的方法。该方法将系统频率、阻尼比、幅值和相位的辨识问题转化为非线性优化问题,引入粒子群优化算法寻找全局最优解。基于粒子群优化的阻尼比和频率辨识方法不需要测量激励信号,原理简单,实现容易。仿真和实验结果表明：基于粒子群优化算法的阻尼比和频率辨识方法不受邻近模态耦合的影响。在无噪声条件下具有较高的辨识精度,随着信噪比的逐步降低,辨识精度开始逐步下降。用低通滤波器滤除高阶模态后,得到的脉冲响应信号对频率、阻尼比、幅值的辨识精度影响很小,对相位的辨识精度影响很大。

     

基于模态应变能分布的压电致动器/传感器位置优化遗传算法

本文由线弹性压电结构有限元动力方程,推导了压电智能结构的振动控制方程。建立了准确模拟层合压电结构动力行为的有限元模型。基于主结构模态应变能分布提出了一种新的优化目标函数,将压电致动器/传感器位置编号作为优化变量,建立了离散变量表示的智能结构优化问题,并通过二进制编码的遗传算法（GA）求解了该最优问题。以四边固支复合层合压电智能板为数值算例,采用比例反馈控制, 研究了最优位置配置致动器/传感器智能结构目标模态的控制效果。数值结果表明基于模态应变能分布的遗传算法所得优化解具有较好的振动控制效果。     

采用混合粒子群算法实现匹配追踪算法

针对匹配追踪信号稀疏分解的巨大计算量问题,在具有全局优化能力的粒子群算法基础上,提出了一种结合BFGS(Broyden、Fletcher、Goldfarb和Shanno)方法和变异操作的混合粒子群算法实现信号匹配追踪分解。利用BFGS方法增强了算法的局部开发能力,加快了信号特征提取速度;通过变异操作控制种群多样性以避免早熟收敛,增强了算法全局探测能力,提高了信号特征提取精度。通过与单一粒子群算法和遗传算法实现仿真信号匹配追踪分解的结果进行对比,证明了使用混合粒子群算法的匹配追踪分解能够快速准确提取信号特征参数。最后,将该算法应用于某内圈损伤轴承振动信号中的冲击特征提取,结果表明该算法在工程应用中具有一定的准确性和实用性。     

基于SSPSO优化GRNN的水电站厂房结构振动响应预测

提出基于优胜劣汰、步步选择的粒子群优化算法(SSPSO),弥补了一般粒子群优化算法容易陷入局部极值、早熟收敛或停滞的缺陷。并运用SSPSO对广义回归神经网络(GRNN)平滑参数P进行优化,充分利用SSPSO寻优能力强及径向基函数调整参数少的优点,建立厂房结构的振动响应预测模型,对某厂顶溢流式水电站的厂坝结构振动响应问题展开预测研究。通过分析预测效果得出:与一般的粒子群算法相比,所提出的SSPSO算法的寻优能力得到了很大的提高。与此同时,基于SSPSO优化的广义回归神经网络(SSPSO-GRNN)与其他网络相比,在预测精度、收敛性能、泛化能力等各个方面得到了很大提升。为水电站厂房振动响应预测提供了新的方法和思路,为增强厂房结构的智能化监测提供了保障。     

A particle swarm optimizer for constrained multi-objective engineering design problems

This article presents a particle swarm optimizer (PSO) capable of handling constrained multi-objective optimization problems. The latter occur frequently in engineering design, especially when cost and performance are simultaneously optimized. The proposed algorithm combines the swarm intelligence fundamentals with elements from bio-inspired algorithms. A distinctive feature of the algorithm is the utilization of an arithmetic recombination operator, which allows interaction between non-dominated particles. Furthermore, there is no utilization of an external archive to store optimal solutions. The PSO algorithm is applied to multi-objective optimization benchmark problems and also to constrained multi-objective engineering design problems. The algorithmic effectiveness is demonstrated through comparisons of the PSO results with those obtained from other evolutionary optimization algorithms. The proposed particle swarm optimizer was able to perform in a very satisfactory manner in problems with multiple constraints and/or high dimensionality. Promising results were also obtained for a multi-objective engineering design problem with mixed variables.     

A new multi-objective particle swarm optimizer using empirical movement and diversified search strategies

Most real-world optimization problems involve the optimization task of more than a single objective function and, therefore, require a great amount of computational effort as the solution procedure is designed to anchor multiple compromised optimal solutions. Abundant multi-objective evolutionary algorithms (MOEAs) for multi-objective optimization have appeared in the literature over the past two decades. In this article, a new proposal by means of particle swarm optimization is addressed for solving multi-objective optimization problems. The proposed algorithm is constructed based on the concept of Pareto dominance, taking both the diversified search and empirical movement strategies into account. The proposed particle swarm MOEA with these two strategies is thus dubbed the empirical-movement diversified-search multi-objective particle swarm optimizer (EMDS-MOPSO). Its performance is assessed in terms of a suite of standard benchmark functions taken from the literature and compared to other four state-of-the-art MOEAs. The computational results demonstrate that the proposed algorithm shows great promise in solving multi-objective optimization problems.     

基于样条逼近的欠驱动航天器姿态运动规划

研究带有两个动量飞轮的刚体航天器姿态控制问题。在系统角动量为零的情况下,系统的控制问题可转化为无漂移系统的非完整运动规划问题。利用最优控制方法和样条逼近技术提出求解带有2个动量飞轮航天器姿态的运动规划控制的粒子群优化算法。运动规划的最优控制是光滑的,且初值和终值均为零,可以方便地通过伺服电机实现飞轮的控制。数值仿真表明:该方法对航天器姿态运动规划控制是有效的。     

A generic framework for handling constraints with agent-based optimization algorithms and application to aerodynamic design

A generic constraint handling framework for use with any swarm-based optimization algorithm is presented. For swarm optimizers to solve constrained optimization problems effectively modifications have to be made to the optimizers to handle the constraints, however, these constraint handling frameworks are often not universally applicable to all swarm algorithms. A constraint handling framework is therefore presented in this paper that is compatible with any swarm optimizer, such that a user can wrap it around a chosen swarm algorithm and perform constrained optimization. The method, called separation-sub-swarm, works by dividing the population based on the feasibility of individual agents. This allows all feasible agents to move by existing swarm optimizer algorithms, hence promoting good performance and convergence characteristics of individual swarm algorithms. The framework is tested on a suite of analytical test function and a number of engineering benchmark problems, and compared to other generic constraint handling frameworks using four different swarm optimizers; particle swarm, gravitational search, a hybrid algorithm and differential evolution. It is shown that the new framework produces superior results compared to the established frameworks for all four swarm algorithms tested. Finally, the framework is applied to an aerodynamic shape optimization design problem where a shock-free solution is obtained.     

A surrogate-assisted particle swarm optimization algorithm based on efficient global optimization for expensive black-box problems

Evolutionary algorithms cannot effectively handle computationally expensive problems because of the unaffordable computational cost brought by a large number of fitness evaluations. Therefore, surrogates are widely used to assist evolutionary algorithms in solving these problems. This article proposes an improved surrogate-assisted particle swarm optimization (ISAPSO) algorithm, in which a hybrid particle swarm optimization (PSO) is combined with global and local surrogates. The global surrogate is not only used to predict fitness values for reducing computational burden but also regarded as a global searcher to speed up the global search process of PSO by using an efficient global optimization algorithm, while the local one is constructed for a local search in the neighbourhood of the current optimal solution by finding the predicted optimal solution of the local surrogate. Empirical studies on 10 widely used benchmark problems and a real-world structural design optimization problem of a driving axle show that the ISAPSO algorithm is effective and highly competitive.     

3D Trajectory Planning of Positioning Error Correction Based on PSO-A* Algorithm

Aiming at the yaw problem caused by inertial navigation system errors accumulation during the navigation of an intelligent aircraft, a three-dimensional trajectory planning method based on the particle swarm optimization-A star (PSO-A*) algorithm is designed. Firstly, an environment model for aircraft error correction is established, and the trajectory is discretized to calculate the positioning error. Next, the positioning error is corrected at many preset trajectory points. The shortest trajectory and the fewest correction times are regarded as optimization goals to improve the heuristic function of A star (A*) algorithm. Finally, the index weights are continuously optimized by the particle swarm optimization algorithm. The optimal trajectory is found by the A* algorithm under the current evaluation index, so the ideal trajectory is planned. The experimental results show that the PSO-A* algorithm can quickly search for ideal trajectories in different environment models, indicating that the algorithm has certain feasibility and adaptability, and verifies the rationality of the proposed trajectory planning model. The PSO-A* algorithm has better convergence accuracy than the A* algorithm, and the search efficiency is significantly better than the grid search A star (GS-A*) algorithm. The PSO-A* algorithm proposed in this paper has certain engineering application value. The researchers will study the realtime and systematic nature of the algorithm.     

基于动态改变权重粒子群算法的球度误差评定

为了实现对球形工件球度误差的精确评定,在4种球度误差评定数学模型的基础上,对文献提供的两组数据采用一种动态改变权重的粒子群算法(PSO)进行计算,这种算法在优化迭代过程中使惯性权重值随粒子的位置和目标函数的性质而更新。与基本PSO算法、最小二乘法、遗传算法和一种改进的PSO算法进行了比较。实验结果显示,相比其他方法,在最小包容区域法模型下使用动态改变权重粒子群算法得到的球度误差最小,第1组数据只需迭代30代左右,约50ms即可收敛,第2组数据收敛也很迅速,且多次实验显示其稳定性很高。因此,所提算法可精确快速地评价球度误差。     

舰艇武器布置问题的一种协同优化算法

针对舰艇武器布置问题的特点，提出了一种基于粒子群优化和分类器系统的协同优化算法，以粒子群优化进行优化计算，用分类器系统消除约束．计算实例表明，该算法能较好地实现优化计算，并能节省大量的计算时间．     

改进粒子群算法在农产品物流配送路径管理中的应用

目的 为了更加合理地进行车辆路径调度管理,提高粒子群求解车辆路径优化问题的性能。方法 提出了一种动态猴子跳跃机制的粒子群优化算法,它借助群体的动态分组,采用不同的动态惯性权重来提高算法的速度,引入猴子跳跃机制来保证全局收敛性。最后把改进算法应用到物流配送路径优化的2个实例中,同一环境下,改进算法搜寻到最优路径适应值、平均运算时间,以及求得最优解的成功次数,均优于标准粒子群优化算法。结果 结果表明,改进的算法能快速有效地确定物流配送路径。结论 改进粒子群优化算法不仅具有较快的寻优速度,而且也提高了算法的收敛性,保证了寻优质量,因此具有很大的应用价值。     

基于PSO-GRNN的直升机旋翼不平衡故障诊断

为了准确诊断直升机旋翼不平衡故障,提出了一种基于粒子群算法和广义回归神经网络模型(PSO-GRNN)的故障诊断方法。将交叉验证得到的平均均方误差作为粒子群的适应度函数,运用粒子群算法搜寻最优的GRNN光滑因子,建立最优的故障诊断模型。结果表明:采用PSO-GRNN模型可实现直升机旋翼不平衡的类型和程度的有效诊断,故障类型准确率高达94.29%,故障程度的诊断最大误差仅6.54%,满足工程需求。