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A feedforward control algorithm for active noise control based on the recursive estimation of a generalized finite impulse response (FIR) filter is presented in this paper. The feedforward control algorithm is applied to a commercial air ventilation silencer to provide active noise compensation in an airduct. A generalized FIR filter has the same linear parameter structure as a taped delay FIR filter that is favorable for (recursive) estimation purposes. However, the advantage of the generalized FIR filters lies in the possibility to include prior knowledge of system dynamics in the tapped delay line of the filter. By comparison with a conventional FIR filter implementation it is shown that a significant improvement in noise cancellation is obtained.

This paper addresses the problem of checking the consistency of experimental closed-loop frequency-domain data with uncertainty models that are structured using coprime factorizations. The uncertainty models presented in this paper use the knowledge of a stabilizing feedback controller to structure and formulate the uncertainty on a model. Subsequently, the controller dependent coprime factor uncertainty model can be used to formulate model (in)validation tests on the basis of closed-loop data. The model (in)validation is performed on sample data from a flexible structure to illustrate the presented model validation results. An open-loop based uncertainty model is also used to demonstrate the benefits of closed-loop uncertainty modeling over open-loop uncertainty modeling.