THD Reduction for Permanent Magnet Synchronous Motor Using Simulated Annealing

Any nonlinear behavior of the system is analyzed by a useful way of Total Harmonic Distortion(THD)technique.Reduced THD achieves lower peak current,higher efficiency and longer equipment life span.Simulated annealing(SA)is applied due to the effectiveness of locating solutions that are close to ideal and to challenge large-scale combinatorial optimization for Permanent Magnet Synchronous Machine(PMSM).The parameters of direct torque controllers(DTC)for the drive are automatically adjusted by the optimization algorithm.Advantages of the PI-Fuzzy-SA algorithm are retained when used together.It also improves the rate of system convergence.Speed response improvement and har-monic reduction is achieved with SA-based DTC for PMSM.This mechanism is known to be faster than other algorithms.Also,it is observed that as compared to other algorithms,the projected algorithm yields a reduced total harmonic distor-tion.As a result of the employment of Space Vector Modulation(SVM)techni-que,the system is resistant to changes in motor specifications and load torque.Through MATLAB&Simulink simulation,the experiment is done and the per-formance is calculated for the controller.

Dependent task assignment algorithm based on particle swarm optimization and simulated annealing in ad-hoc mobile cloud

In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on particle swarm optimization and simulated annealing( PSO-SA) transforms the dependencies between tasks into a directed acyclic graph( DAG) model. The number in each node represents the computation workload of each task and the number on each edge represents the workload produced by the transmission. In order to simulate the environment of task assignment in AMC,mathematical models are developed to describe the dependencies between tasks and the costs of each task are defined. PSO-SA is used to make the decision for task assignment and for minimizing the cost of all devices,which includes the energy consumption and time delay of all devices.PSO-SA also takes the advantage of both particle swarm optimization and simulated annealing by selecting an optimal solution with a certain probability to avoid falling into local optimal solution and to guarantee the convergence speed. The simulation results show that compared with other existing algorithms,the PSO-SA has a smaller cost and the result of PSO-SA can be very close to the optimal solution.

粉末X射线衍射法解析晶体结构的三种常用方法简介

介绍了X射线粉末衍射法解析晶体结构的三种常用方法:直接法、模拟退火法和电荷翻转法的基本原理和处理方法。直接法是典型的倒易空间法,利用倒易空间的衍射强度建立初始结构;模拟退火法则在实空间产生随机模型并进行全局优化获得初始模型;电荷翻转法为双空间迭代法,分别在倒空间和实空间进行微扰并在二者间进行傅里叶循环得到结构模型。文中以三个实例Ba_(3)BPO_(7),MgSO_(4)·2.5H_(2)O以及MgSO_(4)∙2H_(2)O的结构解析分别进行简要说明。

多维QoS约束的云计算工作流调度算法

为有效解决云计算异构系统中工作流调度问题,提出一种多维QoS约束下的改进遗传算法(QoS-IGA)。建立工作流任务调度模型、多维QoS约束模型和考虑任务完成时间、完成费用及虚拟资源可靠性和负载均衡度的多目标优化函数;提出种群初始化原则,以及不破坏任务间依赖关系的交叉与变异算子,引入模拟退火算法的Metropolis准则避免遗传算法的早熟收敛问题。实验结果表明,QoS-IGA算法有效可行,其收敛速度快,调度效率高。

基于DSA-FEM的航空发动机复合材料机匣优化方法

阐述了一种基于直接搜索模拟退火算法(direct simulated annealing,DSA)和有限元方法(finite element method,FEM)的复合材料层合结构的优化方法。对某型航空发动机机匣质量进行质量优化,优化目标为在保证机匣安全的前提下,尽可能减轻机匣质量,优化变量为铺层数和铺层角度。本文详细阐述了DSA优化方法结合FEM的实施步骤,并提出了一种基于多铺层组的复合材料优化技术。结果表明:DSA优化算法非常有效地减轻了机匣的质量。

Antenna geometry strategy with prior information for direction-finding MIMO radars

The antenna geometry strategy for direction finding （DF） with multiple-input multiple-output （MIMO） radars is studied. One case, usually encountered is practical applications, is consi- dered. For a directional antenna geometry with a prior direction, the trace-optimal （TO） criterion （minimizing the trace） on the av- erage Cramer-Rao bound （CRB） matrix is employed. A qualitative explanation for antenna geometry is provided, which is a combi- natorial optimization problem. In the numerical example section, it is shown that the antenna geometries, designed by the proposed strategy, outperform the representative DF antenna geometries.

Optimization of multi-revolution low-thrust transfer based on modified direct method

A modified direct optimization method is proposed to solve the optimal multi-revolution transfer with low-thrust between Earth-orbits. First, through parameterizing the control steering angles by costate variables, the search space of free parameters has been decreased. Then, in order to obtain the global optimal solution effectively and robustly, the simulated annealing and penalty function strategies were used to handle the constraints, and a GA/SQP hybrid optimization algorithm was utilized to solve the parameter optimization problem, in which, a feasible suboptimal solution obtained by GA was submitted as an initial parameter set to SQP for refinement. Comparing to the classical direct method, this novel method has fewer free parameters, needs not initial guesses, and has higher computation precision. An optimal-fuel transfer problem from LEO to GEO was taken as an example to validate the proposed approach. The results of simulation indicate that our approach is available to solve the problem of optimal muhi-revolution transfer between Earth-orbits.

Hybrid Optimization Based PID Controller Design for Unstable System

PID controllers play an important function in determining tuning para-meters in any process sector to deliver optimal and resilient performance for non-linear,stable and unstable processes.The effectiveness of the presented hybrid metaheuristic algorithms for a class of time-delayed unstable systems is described in this study when applicable to the problems of PID controller and Smith PID controller.The Direct Multi Search(DMS)algorithm is utilised in this research to combine the local search ability of global heuristic algorithms to tune a PID controller for a time-delayed unstable process model.A Metaheuristics Algorithm such as,SA(Simulated Annealing),MBBO(Modified Biogeography Based Opti-mization),BBO(Biogeography Based Optimization),PBIL(Population Based Incremental Learning),ES(Evolution Strategy),StudGA(Stud Genetic Algo-rithms),PSO(Particle Swarm Optimization),StudGA(Stud Genetic Algorithms),ES(Evolution Strategy),PSO(Particle Swarm Optimization)and ACO(Ant Col-ony Optimization)are used to tune the PID controller and Smith predictor design.The effectiveness of the suggested algorithms DMS-SA,DMS-BBO,DMS-MBBO,DMS-PBIL,DMS-StudGA,DMS-ES,DMS-ACO,and DMS-PSO for a class of dead-time structures employing PID controller and Smith predictor design controllers is illustrated using unit step set point response.When compared to other optimizations,the suggested hybrid metaheuristics approach improves the time response analysis when extended to the problem of smith predictor and PID controller designed tuning.

基于模拟退火算法的机械臂刚度辨识构型优化与实验

针对机械臂关节刚度辨识的测量构型提出了新的优化方法和实验设计思路。首先,综合考虑机械臂形位以及载荷矢量对刚度辨识的影响,采用参数κ_(F)^(-1)-(A)作为测量构型评价指标。在此基础上,通过合适的模拟退火优化算法得到基于κ_(F)^(-1)-(A)的最优测量构型。基于设计的多向加载装置实现了载荷的优化加载。实验结果表明,与典型评价指标κ_(F)^(-)(J)相比,κ_(F)^(-1)-(A)的最优化测量构型能更好地克服多种测量误差影响,补偿后末端位置精度提高29.59%,最大位置误差降低32.71%。可应用于实际工业环境中的串联机械臂刚度标定。

基于GASA-BP-BiLSTM的轴承故障预测研究

基于BP神经网络收敛速度慢,在预测时易陷入局部极小值陷阱的问题,提出一种基于GASA-BP的预测方法,GA和SA协同优化BP神经网络,使得GASA-BP预测模型同时具备较强的全局搜索能力和跳出局部最优解的能力。BiLSTM是LSTM的一种特殊形式,BiLSTM充分考虑了时间序列数据前后节点之间的相关性,有效提取信号中的时间特征,具有更优秀的分类能力。基于此,提出一种基于GASA-BP-BiLSTM的故障预测模型,并应用于轴承的故障预测中,该模型不仅能够预测出轴承中是否存在故障,还能检测出潜在的故障类型,兼具了预测和故障诊断的优点。

基于混合轴辐式网络的快递运输路径再设计

为了应对当前快递运输量分布的高度离散性和波动性,提出了轴辐式网络和连通式路径相结合的混合轴辐式运输网络,在充分利用已建成的快递网点的前提下,通过在非中心枢纽城市之间开通直达路径来有效降低转运成本。使用包含运输成本、装卸成本和持货成本的成本函数作为优化目标,构建了混合轴辐式快递运输网络的混合整数线性规划模型,开发了改进的遗传-退火算法进行求解:遗传算法求解转运中心位置和非中心城市的分配问题,模拟退火算法求解直发路径的开通问题。提出的模型和算法被应用于基于我国真实快递数据的30个快递仓点的测试算例,相较传统的轴辐式运输方式,平均降低网络总成本32.2%,启发式解与最优解的差距小于5%。此外,灵敏度分析表明,载重1 000 kg的卡车为再设计运输方案的最优交通工具,可以为当前采用轴辐式枢纽网络的快递公司在运输路径的改良问题上提供理论参考和实践证明。

直接搜索—模拟退火算法在水质模型参数识别中的应用

在研究新近发展起来的模拟退火算法及其各种改进算法的基础上,提出并构造了一种能识别水质模型参数的、以记忆为基础的直接搜索—模拟退火算法,并在O connor水质模型参数识别的实例应用中,得到了更优的全局最优解,为水质模型参数识别提供了一条新途径。

基于蚁群优化-模拟退火的天地测控资源联合调度

研究了一类约束条件复杂、具有多时间窗口特性的天地测控资源联合调度问题。建立了涵盖多种约束的问题数学模型和基于测控弧段时间序的无圈有向图模型,提出了一种蚁群优化-模拟退火算法(ACO-SA)求解问题。探讨了两种算法的融合策略,完成了包括状态转移规则、可行解生成策略、信息素更新准则、邻域结构、快速退火计划等关键技术的设计实现。多个算例仿真和结果分析表明,测控弧段时间序能保证ACO-SA得到高质量的优化结果,模拟退火机制能有效提高蚁群优化算法的收敛速度和求解质量,相比于基本遗传算法和先到先服务算法,ACO-SA能得到质量更好的解。

基于模拟退火算法校正天线阵列方向性误差

针对天线阵列方向性误差的校正问题,基于信号子空间的基本原理,结合模拟退火算法,提出了一种精确估计天线阵方向性的方法。该方法使用校正源少,稳定性好,即使在雷达工作、并且存在其它未知信号时,也能准确估计出天线阵的方向性。给出了应用该方法的具体步骤,通过仿真实验证明该方法的有效性。

直驱式电液伺服转叶舵机退火蚁群寻优PD控制

针对直驱式电液伺服转叶舵机转动惯量大、时变不确定性和外负载复杂的特点,提出一种以最大最小蚂蚁算法和模拟退火策略为基础的具有不完全微分的PD控制器的设计方法。该控制器能在线搜索出一组最优PD控制参数Kp*和Kd*作为直驱式电液伺服转叶舵机实时控制中的PD控制器参数。在直驱式电液伺服转叶舵机及加载实验台上进行了阶跃响应和斜坡响应实验。实验结果表明,与传统PD控制器相比,基于退火蚂蚁群算法的PD控制器具有良好的动态性能和鲁棒性,能有效地提高直驱式电液伺服转叶舵机的控制精确度。

基于多参量的模拟退火全局优化傅里叶有限差分算子

傅里叶有限差分法(FFD)能够处理复杂地质构造中的波传播问题,但对陡倾角成像仍有明显的误差.优化参数的方法能够在保持计算效率的前提下进一步提高陡倾角的成像精度.本文在有理近似的基础上,将FFD算子展开式中的常系数由两个拓展为四个,然后采用模拟退火算法对这四个参数进行全局优化.本方法除了考虑速度对比度以外,还考虑了频率和延拓步长等参量的影响.理论误差分析和脉冲响应测试均表明该方法能极大地提高FFD算子的精确传播角度.二维SEG/EAGE盐丘模型实验表明本文方法对陡倾角以及盐下构造的成像精度明显高于未优化的FFD法.将本文的方法与交替方向加插值的方法结合应用于三维脉冲响应测试更进一步证实了本文方法的有效性.

模拟退火遗传算法在DOA估计技术中的应用

将模拟退火思想融入到遗传算法中,形成了另一种优化算法,即模拟退火遗传算法,将其应用于加权子空间(WSF)算法的目标方位(DOA)估计技术中,以求降低WSF算法的运算复杂度,提高DOA估计精度,同时又解决了基本遗传算法在DOA估计中易陷入局部最优、后期搜索迟钝等问题。计算机仿真结果表明:采用模拟退火遗传算法的DOA估计技术在低信噪比条件下比采用基本遗传算法、高斯-牛顿算法有更高的分辨概率,更小的均方误差。

面向拥堵问题的枢纽航线网络优化模型

为解决枢纽机场客流拥堵问题,提高机场运行效率,减少运营成本,提出了一种面向拥堵问题的枢纽航线网络优化模型。该模型基于非严格枢纽航线网络结构,以不同运输方式的费用和流量为约束条件,以枢纽航线网络成本最低为目标,设计了能够减少求解运算的复杂变量表示方法,以及减少陷入局部最优解概率的模拟退火粒子群优化(simulated annealing particle swarm optimization,SAPSO)算法。实验结果表明,相较于严格的枢纽航线网络,所提优化模型能够显著地缓解枢纽机场的拥堵,均衡枢纽机场间客流量,减少网络成本;同时,所提算法具有较快的收敛速度和良好的稳定性。

基于改进遗传算法和序列二次规划的再入轨迹优化

提出结合改进遗传算法和序列二次规划法的可重复使用运载器再入轨迹优化方法,发挥了遗传算法(GA)对初值不敏感和全局收敛性强以及序列二次规划(SQP)法收敛速度快和精度高等优点,弥补了遗传算法优化结果的随机抖动、序列二次规划法对初始值敏感、收敛半径小和容易陷入局部极值等不足.将改进的遗传模拟退火罚函数法用于全局搜索设计空间,序列二次规划法用于局部优化,直接配点法用于将最优控制问题离散为非线性规划问题.算例结果表明,在没有初始估计的情况下,能够得到高精度的全局最优解,证明了该算法的正确性和有效性,验证了该算法具有初值不敏感和鲁棒性好的优点.

模拟退火算法在输电网络扩展规划中的应用

输电网络扩展规划是一个非线性组合优化问题,文中用模拟退火算法有效地解决了该问题。首先用直流潮流模型建立该问题的非线性整数规划模型,然后用模拟退火算法求解该模型的最优解,并对具体实施步骤作了多处优化改进。最后,通过分别对Garver 6节点和IEEE 24节点两个算例的测试,并与Lingo软件得出的计算结果做比较,表明改进的模拟退火算法能更快搜索到较优可行解。