Application of model-matching techniques to feedforward active noise controller design

In this paper, three digital model-matching techniques in U ₂ , H _∞ , and l ₁ performance measures are applied to design digital feedforward controllers for active noise cancellation in ducts. Different measures account for different optimization objectives in terms of physical signals. The distributed nature and high-bandwidth requirements of the control system result in a large set of parameters in plant description and these design techniques proved to be useful in solving the controllers numerically. Experiments were conducted using a floating-point digital signal processor that produced broad-band noise reduction. Design variations and noise reduction effects in terms of human perception are also discussed. It is experimentally proved that using model-matching designs, the causality principle originally raised by Paul Lueg does not have to be satisfied in order to actively reduce the noise level.