Optimization of multichannel feedback filters in an active noise control headrest

Noise reduction and robust stability are two important aspects of the performance to be considered in a multichannel feedback controller. Based on a two-channel active noise control headrest system, the noise reduction and robust stability of a constrained optimization algorithm and two unconstrained optimization algorithms are compared. The theories of these algorithms are introduced first, and then their performance is simulated with the measured transfer functions of an active noise control headrest system. It is shown that the noise reduction performance gradually deteriorates if the robust stability of the system is increased. Under the same robust stability, the constrained optimization algorithm has the highest noise reduction, followed by the generalized leaky FxLMS algorithm, while the regularization algorithm is the lowest.