Multiple input–multiple output adaptive feedback control strategies for the active headrest system: design and real‐time implementation

Abstract

In this article, multiple input-multiple output adaptive feedback control techniques for acoustic noise control in a headrest system are developed. The main goal underlying their design is to provide acoustic comfort to the user, i.e. high noise attenuation level over possibly large areas at the ears. Classical Internal Model Control system does not yield acceptable performance. An approach based on estimates of the residual noise at the ears is then proposed. It is shown that increase in the number of secondary sources to operate for one channel improves the performance. The experiments of tonal noise control are performed on an originally set-up prototype of the active headrest system. The results obtained are illustrated in the promoted form of distribution of zones of quiet.

A Kalman filter approach to virtual sensing for active noise control

“…Note that the implementation illustrated in Figure A.1 was adopted in previous research into virtual sensing methods for active noise control [2][3][4][5][6][7][8][9][10][11][12]. The plant in this figure can be described by the following standard state-space model [14] …”

“…The resulting zones of quiet are usually centered at the error sensors, and are often too small to extend from the error sensors to the observer's ears [1]. To overcome these practical limitations, a number of virtual sensing methods for local active noise control systems have been suggested [2][3][4][5][6][7][8][9][10][11][12]. These methods can be used to obtain estimates of the error signals at locations remote from the physical locations of the error sensors.…”

Virtual sensors for active noise control in acoustic–structural coupled enclosures using structural sensing: Robust virtual sensor design

“…Other works such as that conducted by Wenzel et al (2007) also utilized Kalman filter as a virtual sensor although for non-acoustic automotive control applications. The majority of the work in robust control using virtual sensors was dedicated to diffracted sound field for active headrest applications (e.g., Rafaely and Elliot, 1999;Pawelczyk, 2003) rather than for vibro-acoustic systems. The work concerned the design of robust control algorithms, such as using h 2 /h 1 control method, to accommodate possible changes in system dynamics and not particularly on the robustness of the virtual sensing algorithm itself.…”

Virtual sensors for active noise control in acoustic-structural coupled enclosures using structural sensing: Part II—Optimization of structural sensor placement

“…Moreau et al (2008) reviewed various acoustic virtual sensing methods that were proposed by a number of researchers, e.g., the virtual microphone technique (Elliott and David, 1992), the remote microphone technique (Popovich, 1997;Roure and Albarrazin, 1999), the forward difference prediction and adaptive LMS techniques (Cazzolato, 1999(Cazzolato, , 2002, the Kalman filtering technique , and the virtual sensing technique for a diffused sound field (Moreau et al, 2009). A number of theoretical and experimental studies, such as for active headrest applications (e.g., Rafaely et al, 1999;Pawelczyk, 2003), have demonstrated successful active control implementations for creating a zone of quiet at the virtual sensor location.…”