Active Noise Control Using a Fuzzy Inference System Without Secondary Path Modelling

Kurczyk, Sebastian; Pawełczyk, Marek

doi:10.2478/aoa-2014-0028

Cited by 13 publications

(12 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another idea is to apply adaptive algorithms that do not require plant modeling [41], [42]. It has been found useful to employ fuzzy inference for this purpose [43], [44].…”

Section: Active Casingsmentioning

confidence: 99%

Advances in active control of machinery and device noise

Pawełczyk

2014

Proceedings of 2014 International Conference on Modelling, Identification &Amp; Control

Self Cite

View full text Add to dashboard Cite

Active control has been gaining an increasing role in protecting humans against excessive exposure to lowfrequency acoustic noise. They are designed as supplements to passive isolation techniques or as autonomous solutions. Rapid development of technology including smart materials, electronics, control theory and signal processing algorithms makes active noise control an attractive field for research, development and applications. The plenary lecture presents the problem of machinery and device noise control and author's contribution to this technology. Ideas behind classical and modern control structures and techniques along with guidelines for their choice are demonstrated. Emerging modeling, identification and control problems are also addressed. They are related to complex coupled physical and control phenomena. Concepts and solutions without taking unrealistic assumptions are presented. Perspectives for active control of machinery and device noise are outlined.

show abstract

“…Another idea is to apply adaptive algorithms that do not require plant modeling [41], [42]. It has been found useful to employ fuzzy inference for this purpose [43], [44].…”

Section: Active Casingsmentioning

confidence: 99%

Advances in active control of machinery and device noise

Pawełczyk

2014

Proceedings of 2014 International Conference on Modelling, Identification &Amp; Control

Self Cite

View full text Add to dashboard Cite

show abstract

“…Misleading, for example, when the LLMS algorithm is used for active noise control without secondary path modeling, but with the step-size sign being self-tuned to maintain the stability and convergence. Such novel algorithms are already under development (Kurczyk, Pawelczyk, 2014a;2014b), and use increasing or decreasing error values to decide about the change of the step-size sign. Awareness that a small negative step size may not make the control system output diverge can bring new insight and affect parameterisation of soft computing algorithms employed for tuning parameters of the active control filter without a secondary path model.…”

Section: Introductionmentioning

confidence: 99%

Stability Conditions for the Leaky LMS Algorithm Based on Control Theory Analysis

Bismor¹,

Pawełczyk²

2016

Archives of Acoustics

Self Cite

View full text Add to dashboard Cite

The Least Mean Square (LMS) algorithm and its variants are currently the most frequently used adaptation algorithms; therefore, it is desirable to understand them thoroughly from both theoretical and practical points of view. One of the main aspects studied in the literature is the influence of the step size on stability or convergence of LMS-based algorithms. Different publications provide different stability upper bounds, but a lower bound is always set to zero. However, they are mostly based on statistical analysis. In this paper we show, by means of control theoretic analysis confirmed by simulations, that for the leaky LMS algorithm, a small negative step size is allowed. Moreover, the control theoretic approach alows to minimize the number of assumptions necessary to prove the new condition. Thus, although a positive step size is fully justified for practical applications since it reduces the mean-square error, knowledge about an allowed small negative step size is important from a cognitive point of view.

show abstract

“…A possible solution to those problems can be found in a group of algorithms, which avoid necessity of using a secondary path model [2,3,5,6,9,10]. In particular, Pawełczyk, [6], proposed to use an approximated delay of the secondary path, instead of a whole dynamic model.…”

Section: Introductionmentioning

confidence: 99%

Active noise control without secondary path modelling — Varying-delay LMS approach

Kurczyk

Pawełczyk

2014

2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR)

Self Cite

View full text Add to dashboard Cite

The Filtered-Reference LMS, also known as FxLMS algorithm, is one of the most commonly used adaptive algorithms for noise control systems. It is appreciated due to its simplicity, low computational complexity, and performance efficiency. For its convergence, responses of the acousto-electric secondary path and its model should not differ by more than pi/2 for any frequency contributing to the noise being controlled. Good amplitude matching is, in turn, responsible for convergence rate. Thus, without an acceptable model, the FXLMS algorithm is vulnerable to high excess mean square error or even divergence due to updating control filter parameters in improper direction. The literature presents several recipes, of heuristic or theoretic origin, to cope in circumstances of inaccurate secondary path modeling, or when the path is subject to change during control system operation and thus differs from the model. They usually require additional wideband random excitation to allow for on-line modeling during control system operation. Such excitation deteriorates, however, overall noise reduction results, and the acoustic impression perceived by the user is poor. This paper focuses on an active noise control approach, which requires no secondary path modelling. The convergence is guaranteed by switching sign of the algorithm step size and, in this way, the sign of the parameter update term. It is combined with on-line tunable delay of the reference signal to significantly improve convergence properties of the algorithm. Theoretical justification for this approach is shown for a tonal noise. Then, the method is extended for the case of a narrowband noise. Theoretical consideration is validated by simulations based on data acquired from a real application.

show abstract

Active Noise Control Using a Fuzzy Inference System Without Secondary Path Modelling

Cited by 13 publications

References 15 publications

Advances in active control of machinery and device noise

Advances in active control of machinery and device noise

Stability Conditions for the Leaky LMS Algorithm Based on Control Theory Analysis

Active noise control without secondary path modelling — Varying-delay LMS approach

Contact Info

Product

Resources

About

Active Noise Control Using a Fuzzy Inference System Without Secondary Path Modelling

Cited by 13 publications

References 15 publications

Advances in active control of machinery and device noise

Advances in active control of machinery and device noise

Stability Conditions for the Leaky LMS Algorithm Based on Control Theory Analysis

Active noise control without secondary path modelling &#x2014; Varying-delay LMS approach

Contact Info

Product

Resources

About

Active noise control without secondary path modelling — Varying-delay LMS approach