Comparison of a new rapid convergent adaptive control algorithm to least mean square on an active noise control system

1995 ◽  
Author(s):  
Shozo Koshigoe ◽  
Alan Gordon ◽  
Allen Teagle ◽  
Ching-Hsu Tsay
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Control Algorithm ◽  
Noise Control ◽  
Active Noise Control ◽  
Mean Square ◽  
Least Mean Square ◽  
Active Noise ◽  
Adaptive Control Algorithm

Research of Quantum Algorithm for Adaptive Active Noise Control System

2012 ◽  
Vol 457-458 ◽  
pp. 196-201
Author(s):  
Wei Jiang
Keyword(s):  
Control System ◽  
Adaptive Algorithm ◽  
Noise Control ◽  
Active Noise Control ◽  
Quantum Algorithm ◽  
Adaptive Controller ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Active Noise

Adaptive active noise control based on least mean square (LMS) algorithm is a linear adaptive filter so that it cannot obtain desired noise reduction. Quantum algorithm is combined with noise control to form quantum adaptive controller. Quantum adaptive algorithm is discussed completely and noise control system is simulated in order to analyze the effects of noise control.

The Design of Active Noise Control System in Power Transformer

2013 ◽  
Vol 364 ◽  
pp. 275-279 ◽  
Author(s):  
Chun Ming Pei ◽  
Jiang Tao Liu ◽  
Zhen Yu Liu ◽  
Li Ming Ying
Keyword(s):  
Control System ◽  
Control Algorithm ◽  
Noise Control ◽  
Power Transformer ◽  
Active Noise Control ◽  
Good Control ◽  
Control Effect ◽  
Active Noise ◽  
Experimental Effect ◽  
Adaptive Control Algorithm

Active noise control system on power equipment is designed. The paper gives overall analysis of the structure of the system. Adaptive control algorithm of control system is designed and simulated by CCS3.3. With other parameters of the system fixed, control effect of the system is analyzed by changing the filter order. As can be seen from the experimental effect, good control effect can be obtained when appropriate parameters are selected.

scholarly journals Active Noise Control Using a Functional Link Artificial Neural Network with the Simultaneous Perturbation Learning Rule

2009 ◽  
Vol 16 (3) ◽  
pp. 325-334 ◽  
Author(s):  
Ya-li Zhou ◽  
Qi-zhi Zhang ◽  
Tao Zhang ◽  
Xiao-dong Li ◽  
Woon-seng Gan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Noise Control ◽  
Active Noise Control ◽  
Time Varying ◽  
Mean Square ◽  
Least Mean Square ◽  
Functional Link ◽  
Active Noise ◽  
Artificial Neural

In practical active noise control (ANC) systems, the primary path and the secondary path may be nonlinear and time-varying. It has been reported that the linear techniques used to control such ANC systems exhibit degradation in performance. In addition, the actuators of an ANC system very often have nonminimum-phase response. A linear controller under such situations yields poor performance. A novel functional link artificial neural network (FLANN)-based simultaneous perturbation stochastic approximation (SPSA) algorithm, which functions as a nonlinear mode-free (MF) controller, is proposed in this paper. Computer simulations have been carried out to demonstrate that the proposed algorithm outperforms the standard filtered-x least mean square (FXLMS) algorithm, and performs better than the recently proposed filtered-s least mean square (FSLMS) algorithm when the secondary path is time-varying. This observation implies that the SPSA-based MF controller can eliminate the need of the modeling of the secondary path for the ANC system.

scholarly journals Adaptive Active Noise Suppression Using Multiple Model Switching Strategy

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Quanzhen Huang ◽  
Suxia Chen ◽  
Mingming Huang ◽  
Zhuangzhi Guo
Keyword(s):  
Control System ◽  
Control Algorithm ◽  
Noise Suppression ◽  
Noise Control ◽  
Active Noise Control ◽  
System Response ◽  
Multiple Model ◽  
Switching Strategy ◽  
Online Identification ◽  
Active Noise

Active noise suppression for applications where the system response varies with time is a difficult problem. The computation burden for the existing control algorithms with online identification is heavy and easy to cause control system instability. A new active noise control algorithm is proposed in this paper by employing multiple model switching strategy for secondary path varying. The computation is significantly reduced. Firstly, a noise control system modeling method is proposed for duct-like applications. Then a multiple model adaptive control algorithm is proposed with a new multiple model switching strategy based on filter-u least mean square (FULMS) algorithm. Finally, the proposed algorithm was implemented on Texas Instruments digital signal processor (DSP) TMS320F28335 and real time experiments were done to test the proposed algorithm and FULMS algorithm with online identification. Experimental verification tests show that the proposed algorithm is effective with good noise suppression performance.

scholarly journals Multiple Iteration of Weight Updates for Least Mean Square Adaptive Filter in Active Noise Control Application

2017 ◽  
Vol 95 ◽  
pp. 14006
Author(s):  
Rahimie Mustafa ◽  
Anuar Mikdad Muad ◽  
Shahrizal Jelani ◽  
Ahmad Nur Alifa Abdul Razap
Keyword(s):  
Adaptive Filter ◽  
Noise Control ◽  
Active Noise Control ◽  
Mean Square ◽  
Least Mean Square ◽  
Active Noise ◽  
Control Application
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