Handling large model uncertainty in adaptive feedback noise attenuation by overparametrization

International audienceAdaptive feedback noise attenuation is a very efficient way for strongly attenuating multiple tonal and narrow band disturbances with unknown and time varying characteristics. These adaptive schemes implement the internal model principle for cancelling disturbances combined with the Youla Kucera parametrization which allows to directly tune the disturbance compensation filter without explicit identification of the model of the disturbance. The efficient use of these schemes requires a good knowledge of the model of the compensatory path which can be obtained by experimental system identification. However there are potential applications where the characteristics of the compensatory path may change significantly during operation and this may lead to the instability of the system. The paper addresses the problem of handling large plant model uncertainties by overparametrization of the adaptive disturbance compensation filter. A methodology for designing adaptive feedback noise cancelers in the presence of large model uncertainties is proposed. In addition of the overparametrization a specific design of the linear feedback controller has to be done in order to satisfy a frequency condition in the range of variations of the frequencies characteristics of the model of the compensatory path. The experimental validation of the design is done on a relevant active noise control test bench