基于模糊神经网络的感应电动机转速估计方法

根据感应电动机的数学模型,提出一种基于模糊神经网络的感应电动机转速估计的方法,并给出了速度估计器的模糊神经网络结构和学习算法。仿真结果表明,该方法能准确跟踪电动机实际转速的变化,具有良好的性能。     

感应电动机矢量控制系统的闭环转速估计方法

为降低计算量,提高无速度传感器感应电动机的转速估计速度与磁链的定向准确性,基于感应电动机的电流速度模型,设计了一种简单的闭环速度估计方法,用于无速度传感器感应电动机矢量控制系统中的速度估计.实验结果验证了该方法在感应电动机控制系统应用中的有效性.     

用神经网络实现对感应电机转速估计

根据数学模型．提出了一种利用神经网络进行感应电机转速估计的新方法。方法是采用一个3层神经网络作为速度估计器来估计转速。仿真结果表明，这种基于人工神经网络的转速估计模型可以准确地跟踪感应电机转速的变化，具有良好的动态跟随性能。     

一种基于神经网络逆的感应电动机解耦线性化改进策略

由于感应电动机运行过程中的参数变化,磁场定向控制和解析逆控制所实现的解耦线性化遭到破坏.为此,基于输出为转子磁链幅值和转速的电流控制型感应电动机模型,本文提出了一种神经网络逆解耦线性化方法,理论分析表明,此方法可以实现感应电动机系统的自适应解耦线性化,弱化转子磁链与转速之间的耦合,从而简化外环控制器的设计,进一步提高整个系统控制性能.最后,对采用所提解耦线性化方法的整个感应电动机控制系统进行仿真研究,仿真结果对比表明该解耦线性化方法是有效的.     

矢量控制系统的速度控制及速度辨识新方法研究

介绍了异步电动机矢量控制系统神经网络速度控制器的设计方法;同时提出了将开环直接计算与模型参考自适应方法相结合的神经网络混合转速辨识模型。仿真结果表明,基于该速度控制器和速度估计器的矢量控制系统动静态性能好,解决了瞬时无功模型参考自适应方法的转速不稳定问题,转速估计精度高。     

非正弦供电下异步电动机转速的估计

提出了用BP神经网络来模拟非正弦供电下异步电动机机端电压、电流、功率因数和转速之间的复杂映射关系，实现用无速度传感器估计转速，并用MATLAB中的BP神经网络工具箱进行训练，获取各个神经层的最佳连接权值。仿真和试验结果证明，利用此神经网络的软测量技术来估计转速可达到较高精度。     

基于改进模糊卡尔曼滤波的感应电机转速估计策略研究

针对感应电机转速估计过程中,采集数据易受到外界干扰,导致的转速估计值出现较大偏差的问题,提出采用基于模糊自适应卡尔曼滤波的感应电动机无速度传感器控制策略.通过监视理论残差与实际残差的比值,对量测噪声协方差阵进行递推在线修正,使其逐渐逼近真实噪声水平,从而使滤波器执行最优估计,提高转速估计精度.仿真及实验结果表明,提出的改进模糊卡尔曼估计器,对随机的测量噪声具有较强的抑制能力.能够准确估计电机转速,抗差能力较好,满足工程实际需求.     

感应电动机的自适应控制建模与仿真

本文介绍一种感应电动机的自适应控制仿真.在转子电阻和负载未知的前提下,控制器首先采用二阶滤波环节平滑参考转速指令,然后将系统中某一信号作为虚拟控制信号,进而使系统简化,再应用李亚普诺夫稳定性理论实现该虚拟控制信号,同时对未知参数进行估计.该设计方法对于电机参数的变化具有鲁棒性.在Matlab/Simulink中搭建了感应电动机自适应控制系统的仿真模型,仿真结果表明,用该方法设计的感应电动机控制系统能够获得很好的转速跟踪和磁链跟踪性能.     

感应电动机测速方法及其实现

在感应电动机的直接转矩控制系统中,需要对电动机转速进行闭环控制,从而实现高精度的感应电动机调速,所以转速测量是否精确对系统而言非常关键。利用数字信号处理器TMS320F2812计时频率高,捕捉脉冲简单等优点,为基于TMS320F2812的感应电动机直接控制系统设计了合适的转速测量方法,该方法测速范围宽,精度高,动态响应好,简便可靠。     

电压跌落时感应电动机反馈电流计算及转速影响研究

大容量感应电动机会影响工业配网的短路电流水平,准确计算其反馈电流对系统保护配置及全网短路电流计算具有重要作用。电压跌落时电动机转速变化会影响电动机的反馈电流,不考虑转速变化可能会导致反馈电流计算结果不准确,影响保护的正确动作。利用感应电动机静态等值电路和复常系数线性微分方程组解法求解定转子磁链正负序强制分量和自由分量,进而得到电压跌落时计及转速变化的感应电动机反馈电流计算方法,并进一步探讨了转速对磁链和反馈电流各分量的影响。采用PSCAD/EMTDC仿真软件中感应电动机的5阶动态模型仿真验证了该分析的正确性。     

Speed control of induction motor without rotational transducers

This paper describes a newly developed speed sensorless drive based on neural networks. A backpropagation neural network is used to provide real-time adaptive identification of the motor speed. The estimation objective is the sum of squared errors between a target trajectory and the neural network model output. A backpropagation algorithm is used to adjust the motor speed, so that the neural model output follows the target trajectory. Backpropagation forces the estimated speed to follow precisely the actual motor speed. The zero-speed crossing phenomena is also described, and experimental results are presented and analyzed     

Motor speed identification via neural networks

Speed information is necessary for high-performance vector-controlled induction motor drives. This information is generally provided by a sensor, which spoils the ruggedness and simplicity of the induction motor. This article presents a newly developed speed sensorless drive based on neural network techniques. The backpropagation neural network technique is used to provide a real-time adaptive identification of the motor speed. The estimation objective is defined in terms of a reference or desired trajectory that the neural networks model output should match or track as closely as possible. The backpropagation algorithm is used to adjust the motor speed so that the neural model output follows the desired trajectory. This backpropagation mechanism forces the estimated speed to follow the actual motor speed precisely. This article describes both the theoretical analysis as well as the simulation results to verify the effectiveness of the new method     

自适应神经网络在异步电动机转速辨识中的应用

描述了基于神经网络技术的无速度传感器矢量控制。运用BP神经网络在线对电机转速进行辨识,并以调节电机转速使神经网络输出跟踪参考模型输出。对实际电机进行了仿真验证,证明该方法的可行性和高效性。     

基于模糊神经网络的异步电机转速估计方法

根据异步电机的数学模型,提出基于模糊神经网络的异步电机转速估计方法。将传统参考自适应系统中原有的自适应调节机构用一个具有在线学习能力的模糊神经网络取代,得出了一种基于模糊神经网络的异步电机转速估计方法,并给出了速度估计器的模糊神经网络结构和学习算法。仿真结果表明,该方法能准确跟踪电机实际转速的变化,具有良好的性能。     

Robust speed-controlled induction motor drive based on recurrent neural network

This paper proposes a recurrent neural network speed controller for an induction motor drive. This speed controller consists of a recurrent neural network identifier (RNNI) and recurrent neural network controller (RNNC). The RNNI is used to provide real-time adaptive identification of the unknown motor dynamics. The RNNC is used to produce an adaptive control force so that the motor speed can accurately track the reference command. A back-propagation algorithm was used as the learning algorithm to automatically adjust the weights of the RNNI and RNNC in order to minimize the performance functions. The proposed control scheme can quickly estimate the plant parameters and produce a control force, such that the motor speed can accurately track the reference command. Both computer simulations and experimental results demonstrated that the proposed control scheme was able to obtain robust speed control.     

基于神经网络的直流电动机的调速系统

对估算速度和控制他励直流电动机的人工神经网络(ANN)进行了研究.神经网络控制计划由神经估计、神经控制器两部分组成,这两个网络的训练采用Levenberg -马奎德反向传播算法.标准的三层前馈,输入层和隐藏层使用Sigmoid函数激活,输出层用Purelin函数.仿真结果图形良好,证明了与常规直流电动机控制系统控制方案...     

基于模糊神经网络的纯电动车永磁同步电机矢量控制

纯电动车控制系统对电机控制性能要求较高。提供了一种基于模糊神经网络的永磁同步电机矢量控制方案。以模糊神经网络控制器作为电流调节器,并在速度环引入模糊控制器,将其输出作为电流环的限幅,达到限速的目的。仿真和试验结果表明:对于电动车运行的复杂情况,该方法具有良好的转矩跟踪和电机限速性能。     

Adaptation learning control scheme for a high-performancepermanent-magnet stepper motor using online random training of neuralnetworks

This paper addresses the problem of controlling the speed of a permanent-magnet stepper motor assumed to operate in a high-performance drives environment. An artificial neural network (ANN) control scheme which uses continual online random training (with no offline training) to simultaneously identify and adaptively control the speed of the stepper motor is proposed. The control scheme utilizes two three-layer feedforward ANNs: (1) a tracker identification neural network which captures the nonlinear dynamics of the motor over any arbitrary time interval in its range of operation; and (2) a controller neural network to provide the necessary control actions to achieve trajectory tracking of the motor speed. The inputs to the controller neural network are not constructed from the actual motor/load dynamics, but as a feedback signal, from the estimated state variables of the motor supplied by the neural identifier and the reference trajectory to be tracked by the actual speed. A full nonlinear model (with no simplifying assumptions) is used to model the motor dynamics, and to the best of the authors' knowledge this represents the first such attempt for this device. This paper also makes use of a very realistic and practical scheme to estimate and adaptively learn the noise content in the speed-load torque characteristic of the motor. Simulations reveal that the neural controller adapts and generalizes its learning rate to a wide variety of loads, in addition to providing the necessary abstraction when measurements are contaminated with noise     

用神经元网络进行异步电机转速的辨识和估计

根据异步电动机直接转矩控制原理、提出了采用人工神经元网络速度辨识方法去实现无速度传感器的交流调速控制系统。文介绍了异步电动机直接转矩控制的基本方程和神经网络速度辨识模型,仿真结果表明,系统具有良好的性能。