基于Kriging模型的汽车车门抗撞性优化设计

提出汽车车门抗撞性优化设计的方法,利用拉丁方试验设计选择少量样本点,构建响应目标的近似模型,应用序列二次规划法进行结构优化。对比分析Kriging和多项式响应面两种近似模型对于车门侧撞响应的拟和误差,表明用Kriging模型构建车门抗撞性近似模型是合适的。计算结果表明提出的方法可以保证车门在满足抗撞性要求的前提下,实现轻量化设计。     

卡拉贝利水利枢纽工程坝体填筑优化设计

新疆卡拉贝利水利枢纽工程因泄洪排沙放空洞工程工期滞后一年多,对工程截流及后续坝体施工项目进度产生了重大影响。通过坝体填筑方案的优化设计,不仅使坝体度汛、坝体填筑及蓄水目标能够按照原定进度实现,还节省了工程投资,得到了建设单位、设计单位的一致认可,为土石坝工程施工积累了经验。     

MPC-based dual control with online experiment design

We present two dual control approaches to the model maintenance problem based on adaptive model predictive control (mpc). The controllers employ systematic self-excitation and design experiments that are performed under normal operation, resulting in improved control performance with smaller output variance and less control effort. Our control formulations offer a novel approach to the question of how to excite the plant input to generate informative data within the context of mpc and adaptive control. One controller actively tries to reduce the parameter-estimate error covariances; the other controller maximizes the information in the signals for enhanced learning. Our approach differs from existing ones in that we let our controllers converge to standard certainty equivalence (ce) mpc when the parameter uncertainty decreases or more information is generated, and as a result we avoid plant excitation when the uncertainty is low or enough information has been generated. We demonstrate that the controllers work well with a large number of tuning configurations and also address the issue of models that are not admissible for control design.     

基于不等长序列相似度挖掘的数据关联算法

针对不等长序列数据的关联问题,提出基于滑动窗口的最优匹配增权法不等长序列相似度度量算法。以较短序列作为滑动窗口遍历较长序列得到一组滑动相似度,利用这组相似度形成最优权重,加权得到不等长序列的相似度,并根据相似度大小对序列数据进行关联判决,以解决截断法相似度度量仅能反映截断序列局部相似度的问题。仿真实验验证了所提出算法对不等长序列数据关联的有效性,并对序列长度和量测误差等因素对相似度度量和关联效果的影响进行了讨论。     

基于云计算的多路网络流媒体分布式最优存储与分配策略

针对云计算系统在执行任务过程中的能量消耗过多、处理速度不理想等情况,提出一种基于云计算的多路网络流媒体分布式最优存储与分配策略.通过构建基于云计算的数学模型来分析执行任务时的能量消耗情况,采用基于虚拟调度机制的分布式最优存储策略来实现在满足存储需求的情况下减少服务器进行存储时的能量消耗总量,最小化存储成本,并采用基于动态决策规则的分配策略来根据服务器的功率、性能以及负载情况进行任务调度,使云计算系统在满足服务质量要求的条件下,充分利用系统运作能耗,不产生过多空闲能耗.实验及结果分析表明,所提出的分布式最优存储与分配策略在节省能量消耗、提升运行速度上发挥了较好的效果.     

PMU最优配置及其在舰船电力系统中应用研究

针对舰船电力系统的特点和在潮流方程求解中存在的问题,借鉴在陆地电力系统中配置相应的PMU将使潮流方程直接可解的思想,实现采用遗传和粒子群优化算法的基于潮流可解最优PMU配置方案算法。并对其在舰船电力系统中的有效性和适用性进行了比较,可为舰船电力系统配置PMU提供参考。     

Control of Julia Sets in Generalized Alternated System

In this paper, we first introduce the generalized alternated system. The definition of the Julia set in the generalized alternated system is given, which is called a generalized alternated Julia set. Then, we achieve the control of generalized alternated Julia sets by applying the classic control methods, which are gradient control and optimal control. In addition, the synchronization between two different generalized alternated Julia sets is implemented using gradient control and optimal control. The simulations illustrate the effectiveness and correctness of these two control methods, and the results are displayed in 2D computer graphics.     

农田资源优化分配的计算机仿真

对于既定面积的农田资源,为使所产作物较能针对市场需求,并使种植经济效益最大化,需合理分配不同作物的种植面积.通过分析不同作物的种植成本,存贮成本,结合作物的收成与销售价格,获得各作物的种植经济效益对于种植面积的非线性关系,建立相应的计算机仿真数学模型.为了有效预测来年的作物收成和销售价格,简化假定来年的种植计划不影响来年的销售价格,根据往年的统计数据得到有关的预测数据.对资源分配问题用遗传算法进行优化求解,得到使净收益最大化的种植田亩分配方案结果.分析结论表明,最大经济效益对应的种植面积分配方案有一定的选择范围,更合理的种植面积分配方案必须面向市场需求.     

基于细菌觅食算法的多线圈电- 机械转换器双模控制技术研究

针对提升电-机械转换器快速响应特性的技术需求,提出了新型三线圈结构电-机械转换器。通过建立的电-机械转换器数学模型,提出采用细菌觅食算法来寻找时间最优解,并基于此方法设计了砰-砰与比例-积分双模控制器。分析与仿真结果表明,相对于传统单线圈电-机械转换器,采用双模控制器的三线圈电-机械转换器,其响应时间从8.5 ms缩短至2.5 ms,震荡和超调不超过5%,可知该技术能够实现响应特性,前景良好。     

Optimal Terminal Iterative Learning Control for the Automatic Train Stop System

The train stop control is a typical set‐point control task, where only the final state (i.e., the terminal train stop position) is of concern and specified. For such a control problem, an optimal terminal iterative learning control (TILC) approach is presented in this paper, where the stopping position and initial braking speed are chosen as the terminal system output and the control input, respectively. The controller design only depends on the measured input/output (I/O) data without requiring any modeling information of the train operation system, and the learning gain is updated by the system I/O data iteratively to accommodate the system uncertainties. The monotonic convergence of the terminal tracking error is guaranteed by rigorous mathematical analysis. Extensive simulation results are provided to show the applicability and effectiveness of the proposed approach.