  Many active noise and vibration control systems require models of the control
paths. When the controlled system changes slightly over time, adaptive digital
filters for the identification of the models are useful. This paper aims at the
investigation of a special class of adaptive digital filters: Orthonormal
filter banks possess the robust and simple adaptation of the widely applied
Finite Impulse Response (FIR) filters, but at a lower model order, which is
important when considering implementation on embedded systems. However, the
filter banks require prior knowledge about the resonance frequencies and
damping of the structure. This knowledge can be supposed to be of limited
precision, since in many practical systems, uncertainties in the structural
parameters exist. In this work, a procedure using a number of training systems
to find the fixed parameters for the filter banks is applied. The effect of
uncertainties in the prior knowledge on the model error is examined both with a
basic example and in an experiment. Furthermore, the possibilities to
compensate for the imprecise prior knowledge by a higher filter order are
investigated. Also comparisons with FIR filters are implemented in order to
assess the possible advantages of the orthonormal filter banks. Numerical and
experimental investigations show that significantly lower computational effort
can be reached by the filter banks under certain conditions.
