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

Abstract

The work was aimed to develop a robust virtual sensing design methodology for sensing and active control applications of vibro-acoustic systems. The proposed virtual sensor was designed to estimate a broadband acoustic interior sound pressure using structural sensors, with robustness against certain dynamic uncertainties occurring in an acoustic-structural coupled enclosure. A convex combination of Kalman sub-filters was used during the design, accommodating different sets of perturbed dynamic model of the vibro-acoustic enclosure. A minimax optimization problem was set up to determine an optimal convex combination of Kalman sub-filters, ensuring an optimal worst-case virtual sensing performance. The virtual sensing and active noise control performance was numerically investigated on a rectangular panel-cavity system. It was demonstrated that the proposed virtual sensor could accurately estimate the interior sound pressure, particularly the one dominated by cavitycontrolled modes, by using a structural sensor. With such a virtual sensing technique, effective active noise control performance was also obtained even for the worst-case dynamics.

Structural sensing of interior sound for active control of noise in structural-acoustic cavities

“…Radiation modal amplitudes are squared and multiplied to radiation efficiencies and added to obtain an estimate of the acoustic potential energy. Halim et al (2011a) proposed a strategy for virtual sensing of acoustic pressure by modelling the coupling between the structural and the acoustic domains directly through coupling of the structural and the acoustic modes. The strategy proposed in this work, as explained through Fig.…”

“…It is, however, seen that such a strategy may require a large number of sensors as cavities encountered in practice may have a large number of structural modes contributing to the noise that need to be sensed, making the system unduly complex and uneconomical. The literature also shows that a strategy for structural sensing utilizing Kalman filters has been recently developed for estimating acoustic pressure for local control (Halim et al, 2011a). The sensing strategy proposed by Petersen et al (2008) is based on Kalman filter and is for local control of purely acoustic systems.…”

“…The approach uses a model of the plant and helps to eliminate some of the assumptions like constancy of primary signal at the physical and virtual locations associated with some of the other virtual sensing methods. Halim et al (2011a) proposed an approach for structural sensing of acoustic pressure for local control in a two-way coupled vibro-acoustic system that is uncertain. A virtual sensor is constructed from a convex combination of Kalman sub-filters representing perturbed dynamic models of the system.…”

Robust active sound radiation control of a piezo-laminated composite circular plate of arbitrary thickness based on the exact 3D elasticity model

“…It is also of applied benefit for structural acoustics engineers interested in improvement of low-frequency sound radiation characteristics of thick piezo-composite smart circular plates in the face of uncertainties. 15,19,[41][42] These elements are of realistic concern in advancement of intelligent micro-electromechanical systems, 43,44 for which precise movement control is essential. It can readily be combined with the classical passive control methods 1,2,45,46 and/or expediently be augmented with multiple pairs of electroded piezoelectric actuator/sensor segments in a multi-input multi-output (MIMO) active control framework [47][48][49] to effectively deal with the medium-and high-frequency acoustic radiation problems.…”

Modeling local active sound control with remote sensors in spatially random pressure fields

“…In the development of local active controllers there has also been considerable interest, for practical reasons, in monitoring the sound field within the region of control with sensors outside this region, as reviewed by Moreau et al 13 Originally a virtual microphone technique was proposed for the problem, [13][14][15][16][17] where the primary pressure was assumed to be the same at the sensor and in the control region. Later, systems were developed using the remote microphone technique, 13,18 which assumes a given transfer response between the sensor and control region.…”