Online secondary path modeling in multichannel active noise control systems using variable step size

The problem of acoustic noise is becoming increasingly serious with the growing use of industrial and medical equipment, appliances, and consumer electronics. Active noise control (ANC), based on the principle of superposition, was developed in the early 20th century to help reduce noise. However, ANC is still not widely used owing to the effectiveness of control algorithms, and to the physical and economical constraints of practical applications. In this paper, we briefly introduce some fundamental ANC algorithms and theoretical analyses, and focus on recent advances on signal processing algorithms, implementation techniques, challenges for innovative applications, and open issues for further research and development of ANC systems.

show abstract

“…paths is proposed in [38]. In this section, we focus on the first type, in particular, Zhang's method [27].…”

Section: B) Methods For Improvementmentioning

confidence: 99%

Recent advances on active noise control: open issues and innovative applications

Kajikawa

Gan

Kuo

2012

SIP

304

113

View full text Add to dashboard Cite

show abstract

“…An online identification control algorithm, which has relatively independent identification part and control part is proposed to avoid the mutual influence in . Control strategies, which add noise into the controller output to realize online identification, were studied . The specific performance indexes of the online identification of the secondary path are analyzed in .…”

Section: Introductionmentioning

confidence: 99%

Analysis and implementation of improved multi-input multi-output filtered-X least mean square algorithm for active structural vibration control

Huang

Zhu

Gao

et al. 2013

Struct. Control Health Monit.

View full text Add to dashboard Cite

An improved multi‐input multi‐output filtered‐X least mean square‐based vibration control algorithm is proposed to solve the reference signal extraction problem for active vibration control system. The reference signal is constructed by the controller parameters and the vibration residual signal extracted directly from the vibrating structure, which is related to the external disturbance signal. Meanwhile, an FIR filter is adopted for online identification by adding white noise signal to the controller output as identification input signal; the identified model is substituted into the control algorithm, and the online secondary path identification is realized. Thus, the practical application problem of filtered‐X least mean square algorithm is solved. The simulations and experiments show that the proposed algorithm is reliable and effective with good control performance. Copyright © 2013 John Wiley & Sons, Ltd.

show abstract

“…Secondary source Error sensor feedback especially and the optimal controller might contain unstable poles, it is even impossible to use an FIR adaptive filter to do optimal control without reducing the bandwidth of the controller. Many adaptive online identification methods are reported [14][15][16][17][18]. A functional-link-artificial-neuralnetwork-(FLANN-) based multichannel nonlinear active noise control (ANC) system trained using a particle swarm optimization (PSO) algorithm suitable for nonlinear noise processes is proposed in [19].…”

Section: Noise Sourcementioning

confidence: 99%

Adaptive Active Noise Suppression Using Multiple Model Switching Strategy

Huang

Chen

Huang

et al. 2017

Shock and Vibration

View full text Add to dashboard Cite

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.

show abstract

Online secondary path modeling in multichannel active noise control systems using variable step size

Cited by 32 publications

References 10 publications

Recent advances on active noise control: open issues and innovative applications

Recent advances on active noise control: open issues and innovative applications

Analysis and implementation of improved multi-input multi-output filtered-X least mean square algorithm for active structural vibration control

Adaptive Active Noise Suppression Using Multiple Model Switching Strategy

Contact Info

Product

Resources

About