The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorit...The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorithm.The command input was corrected by weights to generate the desired input for the algorithm,and the feedback was brought into the feedback correction,whose output was the weighted feedback.The weights of the normalized LMS adaptive filtering algorithm were updated on-line according to the estimation error between the desired input and the weighted feedback.Thus,the updated weights were copied to the input correction.The estimation error was forced to zero by the normalized LMS adaptive filtering algorithm such that the weighted feedback was equal to the desired input,making the feedback track the command.The above concept was used as a basis for the development of amplitude phase control.The method has good real-time performance without estimating the system model.The simulation and experiment results show that the proposed amplitude phase control can efficiently cancel the amplitude attenuation and phase delay with high precision.展开更多
The grain production prediction is one of the most important links in precision agriculture. In the process of grain production prediction, mechanical noise caused by the factors of difference in field topography and ...The grain production prediction is one of the most important links in precision agriculture. In the process of grain production prediction, mechanical noise caused by the factors of difference in field topography and mechanical vibration will be mixed in the original signal, which undoubtedly will affect the prediction accuracy. Therefore, in order to reduce the influence of vibration noise on the prediction accuracy, an adaptive Ensemble Empirical Mode Decomposition(EEMD) threshold filtering algorithm was applied to the original signal in this paper: the output signal was decomposed into a finite number of Intrinsic Mode Functions(IMF) from high frequency to low frequency by using the Empirical Mode Decomposition(EMD) algorithm which could effectively restrain the mode mixing phenomenon; then the demarcation point of high and low frequency IMF components were determined by Continuous Mean Square Error criterion(CMSE), the high frequency IMF components were denoised by wavelet threshold algorithm, and finally the signal was reconstructed. The algorithm was an improved algorithm based on the commonly used wavelet threshold. The two algorithms were used to denoise the original production signal respectively, the adaptive EEMD threshold filtering algorithm had significant advantages in three denoising performance indexes of signal denoising ratio, root mean square error and smoothness. The five field verification tests showed that the average error of field experiment was 1.994% and the maximum relative error was less than 3%. According to the test results, the relative error of the predicted yield per hectare was 2.97%, which was relative to the actual yield. The test results showed that the algorithm could effectively resist noise and improve the accuracy of prediction.展开更多
基金Project(50905037) supported by the National Natural Science Foundation of ChinaProject(20092304120014) supported by Specialized Research Fund for the Doctoral Program of Higher Education of China+2 种基金 Project(20100471021) supported by the China Postdoctoral Science Foundation Project(LBH-Q09134) supported by Heilongjiang Postdoctoral Science-Research Foundation,China Project (HEUFT09013) supported by the Foundation of Harbin Engineering University,China
文摘The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorithm.The command input was corrected by weights to generate the desired input for the algorithm,and the feedback was brought into the feedback correction,whose output was the weighted feedback.The weights of the normalized LMS adaptive filtering algorithm were updated on-line according to the estimation error between the desired input and the weighted feedback.Thus,the updated weights were copied to the input correction.The estimation error was forced to zero by the normalized LMS adaptive filtering algorithm such that the weighted feedback was equal to the desired input,making the feedback track the command.The above concept was used as a basis for the development of amplitude phase control.The method has good real-time performance without estimating the system model.The simulation and experiment results show that the proposed amplitude phase control can efficiently cancel the amplitude attenuation and phase delay with high precision.
基金Supported by National Science and Technology Support Program(2014BAD06B04-1-09)China Postdoctoral Fund(2016M601406)Heilongjiang Postdoctoral Fund(LBHZ15024)
文摘The grain production prediction is one of the most important links in precision agriculture. In the process of grain production prediction, mechanical noise caused by the factors of difference in field topography and mechanical vibration will be mixed in the original signal, which undoubtedly will affect the prediction accuracy. Therefore, in order to reduce the influence of vibration noise on the prediction accuracy, an adaptive Ensemble Empirical Mode Decomposition(EEMD) threshold filtering algorithm was applied to the original signal in this paper: the output signal was decomposed into a finite number of Intrinsic Mode Functions(IMF) from high frequency to low frequency by using the Empirical Mode Decomposition(EMD) algorithm which could effectively restrain the mode mixing phenomenon; then the demarcation point of high and low frequency IMF components were determined by Continuous Mean Square Error criterion(CMSE), the high frequency IMF components were denoised by wavelet threshold algorithm, and finally the signal was reconstructed. The algorithm was an improved algorithm based on the commonly used wavelet threshold. The two algorithms were used to denoise the original production signal respectively, the adaptive EEMD threshold filtering algorithm had significant advantages in three denoising performance indexes of signal denoising ratio, root mean square error and smoothness. The five field verification tests showed that the average error of field experiment was 1.994% and the maximum relative error was less than 3%. According to the test results, the relative error of the predicted yield per hectare was 2.97%, which was relative to the actual yield. The test results showed that the algorithm could effectively resist noise and improve the accuracy of prediction.
