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公开(公告)号：US06713978B2
公开(公告)日：2004-03-30
申请号：US10197881
申请日：2002-07-18
IPC分类号： G05B1300
CPC分类号： G06N3/0454 , H02P21/0014 , H02P21/18
摘要： A non-linear, semi-parametric neural network-based adaptive filter is utilized to determine the dynamic speed of a rotating rotor within an induction motor, without the explicit use of a speed sensor, such as a tachometer, is disclosed. The neural network-based filter is developed using actual motor current measurements, voltage measurements, and nameplate information. The neural network-based adaptive filter is trained using an estimated speed calculator derived from the actual current and voltage measurements. The neural network-based adaptive filter uses voltage and current measurements to determine the instantaneous speed of a rotating rotor. The neural network-based adaptive filter also includes an on-line adaptation scheme that permits the filter to be readily adapted for new operating conditions during operations.
摘要翻译： 利用非线性的半参数神经网络自适应滤波器来确定感应电动机内的旋转转子的动态速度，而没有公开使用诸如转速计的速度传感器。 基于神经网络的滤波器是使用实际的电动机电流测量，电压测量和铭牌信息来开发的。 基于神经网络的自适应滤波器使用从实际电流和电压测量得到的估计速度计算器进行训练。 基于神经网络的自适应滤波器使用电压和电流测量来确定旋转转子的瞬时速度。 基于神经网络的自适应滤波器还包括在线适配方案，其允许滤波器在操作期间容易地适应新的操作条件。
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公开(公告)号：US20240213897A1
公开(公告)日：2024-06-27
申请号：US18391734
申请日：2023-12-21
发明人： Florian Hauer , Marko Gecic , Kajetan Nürnberger , Jürgen Schafer
CPC分类号： H02P21/0014 , G06N3/04 , H02P21/18 , H02P21/22
摘要： According to various embodiments, a motor control circuit is described having a controller configured to determine values of a plurality of control voltages for a motor. The motor control circuit includes one or more current sensors configured to measure a plurality of operation currents of the motor and a neural network having a multi-layer perceptron architecture. The neural network is trained to estimate a rotor position of the motor for a current control cycle. The controller is configured to determine values of the plurality of control voltages for the current control cycle using the estimate of the rotor position.
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公开(公告)号：US11876464B2
公开(公告)日：2024-01-16
申请号：US16790055
申请日：2020-02-13
发明人： Shuhui Li , Michael H. Fairbank
CPC分类号： H02P21/0014 , G05B13/027 , G06N3/044 , G06N3/045 , G06N3/084 , H02P21/0025 , H02P21/22 , H02P27/12
摘要： Described herein is a method and system for controlling an interior-mounted permanent magnet (IPM) alternating-current (AC) electrical machine utilizing a space vector pulse-width modulated (SVPWM) converter operably connected between an electrical power source and the IPM AC electrical machine comprising three neural networks (NNs), including a controller NN operably connected to the SVPWM converter, a parameter estimator NN, and a flux-weakening and MTPA NN.
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公开(公告)号：US20180212541A1
公开(公告)日：2018-07-26
申请号：US15879707
申请日：2018-01-25
发明人： Shuhui Li , Xingang Fu , Hoyun Won , Yang Sun
CPC分类号： H02P21/0014 , H02P27/08
摘要： Described herein is an approximate dynamic programming (ADP) vector controller for control of a permanent magnet (PM) motor. The ADP controller is developed using the full dynamic equation of a PM motor and implemented using an artificial neural network (ANN). A feedforward control strategy is integrated with the ANN-based ADP controller to enhance the stability and transient performance of the ADP controller in both linear and over modulation regions. Simulation and hardware experiments demonstrate that the proposed ANN-based ADP controller can track large reference changes with high efficiency and reliability for PM motor operation in linear and over modulation regions.
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公开(公告)号：US20230291340A1
公开(公告)日：2023-09-14
申请号：US18040278
申请日：2021-08-02
发明人： Seiji HASHIMOTO , Masayuki KIGURE , Makoto SHIBUYA
CPC分类号： H02P21/0014 , G06N3/084 , H02P21/13 , H02P21/18 , H02P21/22 , H02P2207/05
摘要： Provided is a motor control device capable of improving efficiency in real time by a neural network structure that directly derives, in a learning manner, an output signal providing optimal efficiency. A motor control device 1 is adapted to control a motor 6, and includes a neural network compensator 11 that receives input signals and repeats learning based on forward propagation and backpropagation thereby to derive an output signal providing optimal efficiency. Input signals are a motor current, a motor parameter and torque, and the like, and output signals are a current command value and a current phase command value. The motor 6 is controlled on the basis of an output signal derived by the neural network compensator 11.
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公开(公告)号：US11705838B2
公开(公告)日：2023-07-18
申请号：US17625777
申请日：2021-03-23
申请人： Jiangsu University
发明人： Huangqiu Zhu , Xin Wang
CPC分类号： H02P21/0014 , H02P21/18 , H02P27/085
摘要： The controller comprises a displacement controller and a rotating speed controller. The displacement controller includes a vibration force compensation control module and a dead-time vibration compensation module. The vibration force compensation control module receives actual displacements and a rotor mechanical angle and outputs corresponding vibration compensation forces. The vibration force compensation control module comprises a first neural network band-pass filter, a second neural network band-pass filter, a third PID controller, and a fourth PID controller. The dead-time vibration compensation module receives a rotor electrical angle and an actual quadrature-direct axis currents and an actual direct axis current and outputs a quadrature-direct axis compensation voltages and a direct axis compensation voltage. The dead-time vibration compensation module consists of a third neural network band-pass filter in a direct axis direction, a fourth neural network band-pass filter in a quadrature axis direction, a sixth PI controller, and a seventh PI controller.
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公开(公告)号：US11695357B2
公开(公告)日：2023-07-04
申请号：US17824314
申请日：2022-05-25
发明人： Kazumasa Shimode
CPC分类号： H02P21/0014 , H02P21/14 , H02P27/06
摘要： A motor control device includes a motor that generates torque corresponding to a current for energizing multi-phase coils, a current sensor that detects a current value of the current for energizing the multi-phase coils, and a controller that obtains a current value of a current flowing through a predetermined coil by adding an origin learning value to a signal input from the current sensor and that controls a current for energizing the predetermined coil based on the current value. The motor control device obtains, each time the origin learning value is changed by a predetermined value, an amplitude of a predetermined order in a q-axis current of the motor based on the changed origin learning value and the signal input from the current sensor, and performs correction based on the origin learning value at the time when the amplitude switches from a decreasing tendency to an increasing tendency.
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8.发明授权Adaptive superconductive magnetic energy storage (SMES) control method and system 有权自适应超导磁能存储（SMES）控制方法和系统公开(公告)号：US08933572B1
公开(公告)日：2015-01-13
申请号：US14018314
申请日：2013-09-04
CPC分类号： H02P9/00 , F03D7/00 , F03D7/02 , F03D9/10 , F03D9/11 , F03D9/25 , H02P21/0014 , Y02E10/723 , Y02E10/725
摘要： The adaptive superconductive magnetic energy storage (SMES) control method and system control a SMES device connected to a power generation system. A radial basis function neural network (RBFNN) connected to the controller adaptively adjusts gain constants of the controller. A processor executes an improved particle swarm optimization (IPSO) procedure to train the RBFNN from input-output training data created by the IPSO, and thereafter generate starting weights for the neural network. Tests carried out show that the proposed adaptive SMES controller maintains the DC capacitor voltage constant, thus improving the efficiency of wind energy transfer. The power output (reactive and real) of the SMES device improves the voltage profile following large voltage dips and provides added damping to the system.
摘要翻译： 自适应超导磁能存储（SMES）控制方法和系统控制连接到发电系统的SMES设备。 连接到控制器的径向基函数神经网络（RBFNN）自适应地调整控制器的增益常数。 处理器执行改进的粒子群优化（IPSO）过程以从由IPSO创建的输入 - 输出训练数据训练RBFNN，然后产生神经网络的起始权重。 进行的测试表明，提出的自适应SMES控制器保持直流电容器电压不变，从而提高风能传递的效率。 SMES器件的功率输出（无功和实际）改善了大电压骤降后的电压分布，并为系统增加了阻尼。
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公开(公告)号：US20030065634A1
公开(公告)日：2003-04-03
申请号：US10197881
申请日：2002-07-18
IPC分类号： G06E001/00 , G06E003/00 , G06N003/02 , G06G007/00 , G06F015/18
CPC分类号： G06N3/0454 , H02P21/0014 , H02P21/18
摘要： A non-linear, semi-parametric neural network-based adaptive filter is utilized to determine the dynamic speed of a rotating rotor within an induction motor, without the explicit use of a speed sensor, such as a tachometer, is disclosed. The neural network-based filter is developed using actual motor current measurements, voltage measurements, and nameplate information. The neural network-based adaptive filter is trained using an estimated speed calculator derived from the actual current and voltage measurements. The neural network-based adaptive filter uses voltage and current measurements to determine the instantaneous speed of a rotating rotor. The neural network-based adaptive filter also includes an on-line adaptation scheme that permits the filter to be readily adapted for new operating conditions during operations.
摘要翻译： 利用非线性的半参数神经网络自适应滤波器来确定感应电动机内的旋转转子的动态速度，而没有公开使用诸如转速计的速度传感器。 基于神经网络的滤波器是使用实际的电动机电流测量，电压测量和铭牌信息来开发的。 基于神经网络的自适应滤波器使用从实际电流和电压测量得到的估计速度计算器进行训练。 基于神经网络的自适应滤波器使用电压和电流测量来确定旋转转子的瞬时速度。 基于神经网络的自适应滤波器还包括在线适配方案，其允许滤波器在操作期间容易地适应新的操作条件。
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