A technique for improved stability of adaptive feedforward controllers without detailed uncertainty measurements

Model errors in adaptive controllers for reduction of broadband noise and vibrations may lead to unstable systems or increased error signals. Previous work has shown that the addition of a low-authority controller that increases damping in the system may lead to improved performance of an adaptive, high-authority controller. Other researchers have suggested to use frequency dependent regularization based on measured uncertainties. In this paper an alternative method is presented that avoids the disadvantages of these methods namely the additional complex hardware, and the need to obtain detailed information of the uncertainties. An analysis is made of an active noise control system in which a difference exists between the secondary path and the model as used in the controller. The real parts of the eigenvalues that determine the stability of the system are expressed in terms of the amount of uncertainty and the singular values of the secondary path. Based on these expressions, modifications of the feedforward control scheme are suggested that aim to improved performance without requiring detailed uncertainty measurements. For an active noise control system in a room it is shown that the technique leads to improved performance in terms of robustness and the amount of reduction of the error signals.