Central nerve signal evoked by thoughts can be directly used to control a robot or prosthetic devices without the involvement of the peripheral nerve and muscles.This is a new strategy of human-computer interaction.A ...Central nerve signal evoked by thoughts can be directly used to control a robot or prosthetic devices without the involvement of the peripheral nerve and muscles.This is a new strategy of human-computer interaction.A method of electroencephalogram(EEG) phase synchronization combined with band energy was proposed to construct a feature vector for pattern recognition of brain-computer interaction based on EEG induced by motor imagery in this paper,rhythm and beta rhythm were first extracted from EEG by band pass filter and then the frequency band energy was calculated by the sliding time window;the instantaneous phase values were obtained using Hilbert transform and then the phase synchronization feature was calculated by the phase locking value(PLV) and the best time interval for extracting the phase synchronization feature was searched by the distribution of the PLV value in the time domain.Finally,discrimination of motor imagery patterns was performed by the support vector machine(SVM).The results showed that the phase synchronization feature more effective in4s-7s and the correct classification rate was 91.4%.Compared with the results achieved by a single EEG feature related to motor imagery,the correct classification rate was improved by 3.5 and4.3 percentage points by combining phase synchronization with band energy.These indicate that the proposed method is effective and it is expected that the study provides a way to improve the performance of the online real-time brain-computer interaction control system based on EEG related to motor imagery.展开更多
针对双三相永磁同步电机模型预测共模电压抑制方法存在寻优计算量大、开关频率较高、稳态性能不佳的问题,提出一种改进型模型预测电流控制.首先,改进六相两电平逆变器,降低零矢量共模电压幅值;其次,选择小共模电压矢量构造虚拟电压矢量...针对双三相永磁同步电机模型预测共模电压抑制方法存在寻优计算量大、开关频率较高、稳态性能不佳的问题,提出一种改进型模型预测电流控制.首先,改进六相两电平逆变器,降低零矢量共模电压幅值;其次,选择小共模电压矢量构造虚拟电压矢量,简化价值函数的同时减小共模电压和电流谐波含量;再次,通过计算参考电压矢量直接选择最优电压矢量以减少寻优次数,并引入占空比控制提升电机控制精度,改善电机稳态性能.最后,仿真对比传统模型预测电流控制、RCMV(Reduced Common Mode Voltage)-1、RCMV-2和所提控制方法.结果表明,所提控制方法在减小共模电压的同时,降低了转矩脉动和谐波电流,且较RCMV-2方法开关频率明显降低;此外,寻优代码执行时间相较于RCMV-1和RCMV-2分别降低了约91%和65%,减小了计算量.展开更多
基金supported by the National Natural Science Foundation of China(81470084,61463024)the Research Project for Application Foundation of Yunnan Province(2013FB026)+2 种基金the Cultivation Program of Talents of Yunnan Province(KKSY201303048)the Focal Program for Education Department of Yunnan Province(2013Z130)the Brain Information Processing and Brain-computer Interaction Fusion Control of Kunming University Scienceand Technology(Fund of Discipline Direction Team)
文摘Central nerve signal evoked by thoughts can be directly used to control a robot or prosthetic devices without the involvement of the peripheral nerve and muscles.This is a new strategy of human-computer interaction.A method of electroencephalogram(EEG) phase synchronization combined with band energy was proposed to construct a feature vector for pattern recognition of brain-computer interaction based on EEG induced by motor imagery in this paper,rhythm and beta rhythm were first extracted from EEG by band pass filter and then the frequency band energy was calculated by the sliding time window;the instantaneous phase values were obtained using Hilbert transform and then the phase synchronization feature was calculated by the phase locking value(PLV) and the best time interval for extracting the phase synchronization feature was searched by the distribution of the PLV value in the time domain.Finally,discrimination of motor imagery patterns was performed by the support vector machine(SVM).The results showed that the phase synchronization feature more effective in4s-7s and the correct classification rate was 91.4%.Compared with the results achieved by a single EEG feature related to motor imagery,the correct classification rate was improved by 3.5 and4.3 percentage points by combining phase synchronization with band energy.These indicate that the proposed method is effective and it is expected that the study provides a way to improve the performance of the online real-time brain-computer interaction control system based on EEG related to motor imagery.
文摘针对双三相永磁同步电机模型预测共模电压抑制方法存在寻优计算量大、开关频率较高、稳态性能不佳的问题,提出一种改进型模型预测电流控制.首先,改进六相两电平逆变器,降低零矢量共模电压幅值;其次,选择小共模电压矢量构造虚拟电压矢量,简化价值函数的同时减小共模电压和电流谐波含量;再次,通过计算参考电压矢量直接选择最优电压矢量以减少寻优次数,并引入占空比控制提升电机控制精度,改善电机稳态性能.最后,仿真对比传统模型预测电流控制、RCMV(Reduced Common Mode Voltage)-1、RCMV-2和所提控制方法.结果表明,所提控制方法在减小共模电压的同时,降低了转矩脉动和谐波电流,且较RCMV-2方法开关频率明显降低;此外,寻优代码执行时间相较于RCMV-1和RCMV-2分别降低了约91%和65%,减小了计算量.
