Adaptive Feedforward Control Schemes in Time-Domain, Frequency-Domain and Wavelet Transform Domain

1995 ◽  
Vol 28 (13) ◽  
pp. 291-296
Author(s):  
J. Xin ◽  
N. Ojiro ◽  
T. Murakami ◽  
H. Ohmori ◽  
A. Sano
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Feedforward Control ◽  
Transform Domain ◽  
Control Schemes ◽  
Adaptive Feedforward

scholarly journals Two De-Noising Methods Based on Gabor Transform

2011 ◽  
Vol 2-3 ◽  
pp. 176-181
Author(s):  
Yong Jun Shen ◽  
Guang Ming Zhang ◽  
Shao Pu Yang ◽  
Hai Jun Xing
Keyword(s):  
Wavelet Transform ◽  
Adaptive Filtering ◽  
Time Domain ◽  
Background Noise ◽  
Averaging Method ◽  
Vibration Signal ◽  
Gabor Transform ◽  
Transform Domain ◽  
Computation Scheme ◽  
Source Signals

Two de-noising methods, named as the averaging method in Gabor transform domain (AMGTD) and the adaptive filtering method in Gabor transform domain (AFMGTD), are presented in this paper. These two methods are established based on the correlativity of the source signals and the background noise in time domain and Gabor transform domain, that is to say, the uncorrelated source signals and background noise in time domain would still be uncorrelated in Gabor transform domain. The construction and computation scheme of these two methods are investigated. The de-noising performances are illustrated by some simulation signals, and the wavelet transform is used to compare with these two new de-noising methods. The results show that these two methods have better de-noising performance than the wavelet transform, and could reduce the background noise in the vibration signal more effectively.

Synchronization of Two Motion Control Axes Under Adaptive Feedforward Control

1992 ◽  
Vol 114 (2) ◽  
pp. 196-203 ◽  
Author(s):  
Masayoshi Tomizuka ◽  
Jwu-Sheng Hu ◽  
Tsu-Chih Chiu ◽  
Takuya Kamano
Keyword(s):  
Motion Control ◽  
Time Domain ◽  
Adaptive Systems ◽  
Feedforward Control ◽  
Synchronization Error ◽  
Adaptive Synchronization ◽  
Motion Error ◽  
Feedforward Controller ◽  
The Difference ◽  
Adaptive Feedforward

In this paper, motion synchronization of two d-c motors, or motion control axes, under adaptive feedforward control is considered. The adaptive feedforward control system for each axis consists of a proportional feedback controller, an adaptive disturbance compensator and an adaptive feedforward controller. If the two adaptive systems are left uncoupled, a disturbance input applied to one of the two axes will cause a motion error in the disturbed axis only, and the error becomes the synchronization error. To achieve a better synchronization, a coupling controller, which responds to the synchronization error, i.e., the difference between the two motion errors, is introduced. In this case, when a disturbance input is applied to one axis, the motion errors appear in the undisturbed axis as well as in the disturbed axis. The motion error in the undisturbed axis is introduced by the coupling controller and the adaptive feedforward controller. The adaptive synchronization problem is formulated and analyzed in the continuous time domain first, and then in the discrete time domain. Stability conditions are obtained. Effectiveness of the adaptive synchronization controller is demonstrated by simulation.

Adaptive Digital Filtering and Its Algorithms for Acoustic Echo Canceling

2011 ◽  
pp. 225-285
Author(s):  
Mohammad Reza Asharif ◽  
Rui Chen
Keyword(s):  
Correlation Function ◽  
Wavelet Transform ◽  
Computational Complexity ◽  
Frequency Domain ◽  
Time Domain ◽  
Digital Filtering ◽  
Wiener Filtering ◽  
Acoustic Echo ◽  
The Time Domain ◽  
New System

In this chapter, we shall study adaptive digital filtering (ADF) and its application to acoustic echo canceling (AEC). At first, Wiener filtering and algorithms such as LMS in the time domain for ADF are explained. Then, to decrease the computational complexity, the frequency domain algorithms such as FDAF and FBAF will be studied. To challenge the double-talk problem in AEC, we will also introduce various algorithms by processing the correlation function of the signal. The proposed algorithms here are CLMS, ECLMS, and using frequency domain is FECLMS, and using wavelet transform is WECLMS. Each of these algorithms has its own merits, and they will be evaluated. At the end of this chapter a new system for room-acoustic partitioning is proposed. This new system is called smart acoustic room (SAR). The SAR will also be used in AEC with double-talk condition. The authors wish to gather all aspects in studying ADF and their use in AEC by going very deep into theoretical details as well as considering more practical and feasible applications considering real-time implementation.

Analysis of Tire Tread Pattern’s Impact on Interior Vibration and Noise Based on Wavelet Transform

2011 ◽  
Vol 66-68 ◽  
pp. 1755-1761 ◽  
Author(s):  
Jian Feng He ◽  
Xiao Xiong Jin ◽  
Wan Ying Wang
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Frequency Analysis ◽  
Time Domain ◽  
Signal Reconstruction ◽  
Load Condition ◽  
Morlet Wavelet ◽  
Time Frequency ◽  
Time Domain Signal ◽  
Distribution Features

The path of tire pattern’s impact on interior noise and vibration was analyzed. The same type tires with six kinds of different tread pattern were selected as the investigating subjects, and the interior measuring points’ vibration and noise signals of different tires were measured under full load condition. The basic theory of Wavelet Transform (WT) and its signal reconstruction principle were researched, and 1/3 octave Morlet wavelet was induced and analyzed. The vibration and noise signals of measuring points were transformed by using 1/3 octave Morlet wavelet, distribution features of vibration and noise signals of six tires with different tread pattern were made a comparison in time domain and frequency domain, and the distinct results of different tires were achieved. The advantage of WT in the aspect of time-frequency analysis for time domain signal is revealed.

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