复数型扩展卡尔曼滤波算法在感应电机 直接转矩控制系统中的应用

针对传统算法中存在的数字信号处理器(DSP)运算速度要求高因而容易产生较大的延迟的问题.提出一种复数型扩展卡尔曼滤波观测器(ECKF)对感应电机进行状态估计,将得到的定子磁链和电机转速应用于直接转矩控制系统中,实现感应电机的无速度传感器控制.采用感应电机复数模型进行滤波器设计可以简化感应电机状态方程的维数并有效减少滤波算法计算量.由于复数型扩展卡尔曼滤波器在实现过程中没有矩阵求逆的运算,并且与常规扩展卡尔曼滤波器相比具有更低的维数,因此DSP的运算时间得到了有效的降低,提高了滤波器状态估计的快速性.仿真和实验结果验证了所提出的复数型扩展卡尔曼滤波器有效性和可行性.     

基于粒子群优化算法的无传感器调速系统研究

针对扩展卡尔曼滤波器(EKF)中噪声协方差阵对估计准确性影响较大,且选取较困难的问题,提出基于粒子群优化(PSO)算法优化协方差矩阵的方法.使用EKF估计永磁同步电机(PMSM)的转速,实现PMSM无速度传感器控制,并利用PSO优化噪声协方差阵.采用STM32F407ZET6作为核心控制器构建控制系统,实验结果表明:使用PSO的EKF观测器能以较高的精度估计转速,实现精确控制.     

EKF在无速度传感器感应电机中的应用

在交流感应电机的状态方程的基础上,将交流感应电机的转速看成状态变量,利用较容易检测到的定子电流及定子电压信号,运用扩展卡尔曼滤波器估计出感应电机的速度.     

六维力传感器下E膜小生境野草EKF滤波研究

为减小噪声信号对六维力传感器测量精度的影响,同时解决因主振型信息缺失导致扩展Kalman滤波器难以获得最优系统估计的问题,提出一种基于小生境野草算法优化的扩展卡尔曼滤波（NIWO-EKF）算法。算法根据正弦激励力响应与应变之间的关系,构建六维力传感器下E型膜非线性系统模型。将系统干扰矩阵与控制矩阵视为一个整体,引入野草繁殖思想,以前6阶主振型信息构成的综合矩阵为均值,进行高斯采样,产生初始化的可行解。将小生境技术与野草算法相融合,利用野草算法进行全局搜索,根据适应度的大小对个体进行降序排列,按照小生境容量划分出多个种群协同合作,避免搜索过程陷入局部最优,提高算法的寻优精度和收敛速度。采用改进野草算法对EKF中的系统干扰控制矩阵进行优化处理。仿真实例表明,优化后的扩展卡尔曼滤波器能有效地提高六维力传感器的测量精度,具有很好的鲁棒性和稳定性。     

一种基于遗传算法的FastSLAM2.0算法

FastSLAM2.0算法的重采样过程会带来“粒子耗尽”问题,为了改进算法的性能、提高估计精度,将FastSLAM2.0算法与遗传算法相结合,提出了一种解决SLAM问题的方法——遗传快速SLAM算法．针对FastSLAM2.0算法的特点,设计了一种改进的遗传算法来兼顾粒子权值和粒子集的多样性．遗传快速SLAM算法采用unscented粒子滤波器估计机器人的路径,地图估计则采用扩展卡尔曼滤波器．采用SLAM领域的标准数据集“carparkdataset”对提出的算法进行了验证,实验结果表明遗传快速SLAM算法在估计精度和一致性方面都具有较好的性能,并且算法的计算复杂度能满足实时性要求．     

无传感器PMSM中基于IGSO优化EKF的速度估计方法

为了提高无传感器永磁同步电机(PMSM)控制系统中速度控制性能,提出一种基于改进群搜索优化(IGSO)算法的扩展卡尔曼滤波(EKF)速度估计方案。首先,分析了PMSM磁场定向控制(FOC)系统模型;然后,将电机的d-q轴电压、电流和转子速度作为状态变量,构建EKF中的状态方程来估计转速和负载。同时,为了提高EKF的估计性能,以估计值与实际值的平方误差积分(ISE)作为适应度函数,通过IGSO算法来优化EKF中的噪声协方差矩阵Q和R,以此获得最优参数。仿真结果表明,提出的控制系统能够精确估计出电机转速并进行有效控制。     

非高斯噪声中的粒子滤波算法研究

在非线性非高斯动态系统中,粒子滤波已成为解决系统参数估计和状态滤波的主流方法。然而,粒子退化是粒子滤波中不可避免的现象,粒子重采样是解决方法之一。本文针对粒子退化现象,在扩展卡尔曼滤波器的基础上研究了一种基于支持向量机粒子滤波算法,算法实现中扩展卡尔曼粒子滤波器结合支持向量机对当前时刻的重要性采样,再对粒子样本进行重采样。该算法能有效地利用量测值的最新信息,状态估计误差较小,同时避免了粒子匮乏。理论分析和仿真结果表明,新算法在双模噪声非线性系统估计的精度优于标准粒子滤波算法与扩展卡尔曼粒子滤波算法。     

动载体环境视觉惯性自适应融合物体姿态测量算法

针对动载体环境下视觉惯性组合姿态测量的过程中,惯性器件噪声时变和突变干扰会使卡尔曼滤波器估计不准确的问题,研究了一种Sage-Husa自适应强跟踪扩展卡尔曼算法。算法采用Sage-Husa自适应滤波来实时估计惯性器件的噪声协方差,引入多重渐消因子来抑制突变干扰,增强融合算法的鲁棒性。实验证明,当存在系统噪声变化和突变干扰时,所提算法优于Sage-Husa自适应扩展卡尔曼算法与强跟踪扩展卡尔曼算法,具有良好的鲁棒性。     

基于混合卡尔曼粒子滤波算法的期权定价方法

针对金融领域的期权定价问题,为提高粒子滤波算法对期权价格的估计精度,提出使用混合卡尔曼粒子滤波算法(MKPF)进行期权价格预测,该算法使用Unscented 卡尔曼滤波器和扩展卡尔曼滤波器作为混合建议分布产生重要采样密度.在某一时刻,每一个粒子首先经过Unscented卡尔曼滤波器更新得到一个状态估计值,然后以该估计值作为扩展卡尔曼滤波器的先验估计再次更新粒子,得到该时刻最终的估计值.实验中针对经典的Black-Scholes期权定价公式,使用包括MKPF算法在内的4种算法对期权价格进行预测,结果表明MKPF算法预测的期权价格与真实期权价格的误差最小,证明了MKPF算法在期权定价问题中的有效性.     

多模噪声中粒子滤波算法研究

粒子滤波是一种解决非高斯滤波问题的有效方法,受到许多领域的研究人员的重视。在扩展卡尔曼滤波(EKF)的基础上,提出一种基于多层感知器(MLP)的扩展卡尔曼滤波算法。利用扩展卡尔曼粒子滤波器和MLP对当前时刻状态重要性采样,引入MLP对样本进行重采样。该算法能有效利用测量值的最新信息,对状态估计的误差更小。在实验中,对于多模噪声非线性系统,该算法与另外算法进行比较。结果证明,所提算法性能优异于其他算法。     

Multi-objective parameter estimation of induction motor using particle swarm optimization

In order to simplify the offline parameter estimation of induction motor, a method based on optimization using a particle swarm optimization (PSO) technique is presented. Three different induction motor models such as approximate, exact and deep bar circuit models are considered. The parameter estimation methodology describes a method for estimating the steady-state equivalent circuit parameters from the motor performance characteristics, which is normally available from the manufacturer data or from tests. The optimization problem is formulated as multi-objective function to minimize the error between the estimated and the manufacturer data. The sensitivity analysis is also performed to identify parameters, which have the most impact on motor performance. The feasibility of the proposed method is demonstrated for two different motors and it is compared with the genetic algorithm and the classical parameter estimation method. Simulation results show that the proposed PSO method was indeed capable of estimating the parameters over a wide operating range of the motor.     

基于模式优选思想改进的粒子群优化算法

针对粒子群优化算法（PSO）容易陷入局部最优值的缺点，提出一种基于遗传算法模式定理思想改进的粒子群优化算法（IPSO）．新算法改善了粒子群优化算法摆脱局部极小点的能力．对典型函数的测试表明，IPSO算法的全局搜索能力有了显著提高，特别是对多峰函数能有效地避免早熟收敛问题．将改进的粒子群优化算法用于氧化反应动力学参数的优化，计算结果表明，新算法优化结果明显优于文献报道．     

An improved particle swarm optimization algorithm based on comparative judgment

Particle swarm optimization (PSO) algorithm is one of the most effective and popular swarm intelligence algorithms. In this paper, based on comparative judgment, an improved particle swarm optimization (IPSO) is proposed. Firstly, a new search equation is developed by considering individual experience, social experience and the integration of individual and social experience, which can be used to improve the convergence speed of the algorithm. Secondly, in order to avoid falling into a local optima, a location abandoned mechanism is proposed; meanwhile, a new equation to generate a new position for the corresponding particle is proposed. The experimental results show that IPSO algorithm has excellent solution quality and convergence characteristic comparing to basic PSO algorithm and performs better than some state-of-the-art algorithms on almost all tested functions.     

应用微粒群算法确定有机化合物分子式

针对基本微粒群优化算法（PSO）存在容易陷入局部最优和收敛速度慢的缺点,在整数空间使用带收缩因子的微粒群优化算法基础上,提出了一种带变异概率的微粒群优化算法（IPSO）,用于提高微粒群的多样性,避免算法陷入局部最优解。实验证明,改进后的微粒群优化算法在防止早熟和加快收敛方面优于基本PSO算法和基本PSO算法加一半微粒随机初始化算法（PSO_HPO算法）。IPSO算法应用到确定有机化合物分子式时,取得了很好的效果。     

一种改进的粒子群优化算法及其仿真

粒子群优化算法(particle swarm optimization,PSO)是一种新兴的优化技术,其思想来源于人工生命和演化计算理论。PSO算法具有简单、易实现、可调参数少等特点,在很多领域得到了广泛应用。但PSO算法存在早熟收敛问题。为了克服粒子群优化算法的早熟收敛问题,提出了一种旨在保持种群多样性的改进PSO(IPSO)算法,以提高PSO算法摆脱局部极小点的能力。通过对3种Benchmark函数的测试,结果表明IPSO算法不仅具有较快的收敛速度、有效的全局收敛性能,而且还具有良好的稳定性。     

Solving nonlinear optimal control problems using a hybrid IPSO-SQP algorithm

A hybrid algorithm by integrating an improved particle swarm optimization (IPSO) with successive quadratic programming (SQP), namely IPSO-SQP, is proposed for solving nonlinear optimal control problems. The particle swarm optimization (PSO) is showed to converge rapidly to a near optimum solution, but the search process will become very slow around global optimum. On the contrary, the ability of SQP is weak to escape local optimum but can achieve faster convergent speed around global optimum and the convergent accuracy can be higher. Hence, in the proposed method, at the beginning stage of search process, a PSO algorithm is employed to find a near optimum solution. In this case, an improved PSO (IPSO) algorithm is used to enhance global search ability and convergence speed of algorithm. When the change in fitness value is smaller than a predefined value, the searching process is switched to SQP to accelerate the search process and find an accurate solution. In this way, this hybrid algorithm may find an optimum solution more accurately. To validate the performance of the proposed IPSO-SQP approach, it is evaluated on two optimal control problems. Results show that the performance of the proposed algorithm is satisfactory.     

An improved particle swarm optimization algorithm for flowshop scheduling problem

The flowshop scheduling problem has been widely studied and many techniques have been applied to it, but few algorithms based on particle swarm optimization (PSO) have been proposed to solve it. In this paper, an improved PSO algorithm (IPSO) based on the “alldifferent” constraint is proposed to solve the flow shop scheduling problem with the objective of minimizing makespan. It combines the particle swarm optimization algorithm with genetic operators together effectively. When a particle is going to stagnate, the mutation operator is used to search its neighborhood. The proposed algorithm is tested on different scale benchmarks and compared with the recently proposed efficient algorithms. The results show that the proposed IPSO algorithm is more effective and better than the other compared algorithms. It can be used to solve large scale flow shop scheduling problem effectively.     

Non-linear system control using a recurrent fuzzy neural network based on improved particle swarm optimisation

This article introduces a recurrent fuzzy neural network based on improved particle swarm optimisation (IPSO) for non-linear system control. An IPSO method which consists of the modified evolutionary direction operator (MEDO) and the Particle Swarm Optimisation (PSO) is proposed in this article. A MEDO combining the evolutionary direction operator and the migration operation is also proposed. The MEDO will improve the global search solution. Experimental results have shown that the proposed IPSO method controls the magnetic levitation system and the planetary train type inverted pendulum system better than the traditional PSO and the genetic algorithm methods.     

基于IKGC-PSO算法的无人机三维路径规划系统

为了解决标准粒子群算法在无人机三维路径规划中存在的易陷入局部最优、动态化不足和路径平滑性差等问题,提出了一种基于粒子群算法和遗传算法的,融入K均值精英化和柯西变异的优化算法;采用K均值聚类算法进行精英初始化,优化粒子种群的分布;动态化学习因子,强化惯性权重的全局性,保留粒子群算法收敛速度快的优点;融入遗传思想,采用柯西变异的方法,提高寻解最优解的能力;在对比实验中,模拟了实际的复杂三维环境,选取了路径总长度、飞行高度差以及马尔科夫生存状态组成目标函数;结果表明改进算法的鲁棒性提高了98%,求解质量相较于IPSO算法和PSO算法分别提高了5.8%和10.6%,验证了优化后方法的有效性和鲁棒性。     

一种改进的动量粒子群算法及实验分析

为了克服粒子群算法存在的收敛缓慢、后期振荡等缺陷,在基本粒子群算法的基础上,引入动量项,提出一种新的改进型粒子群算法.新算法中动量项与微粒的历史修正量线性相关,典型复杂优化函数的实验结果表明:该算法不但保持了基本粒子群算法的简单、易实现等优点,而且提高了算法的收敛速度及部分地避免了算法的后期振荡.