In this paper, a multichannel adaptive control algorithm is described which has good convergence properties while having relatively small computational complexity. This complexity is similar to that of the filtered-error algorithm. In order to obtain these properties, the algorithm is based on a preprocessing step for the actuator signals using a stable and causal inverse of the minimum-phase part of the transfer path between actuators and error sensors, the secondary path. The latter algorithm is known from the literature as postconditioned filtered-error algorithm, which improves convergence rate for the case that the minimum-phase part of the secondary path increases the eigenvalue spread. However, the convergence rate of this algorithm suffers from delays in the adaptation path because adaptation rates have to be reduced for larger delays. The contribution of this paper is to modify the postconditioned filtered-error scheme in such a way that the adaptation rate can be set to a higher value. Consequently, the scheme also provides good convergence if the system contains significant delays. Furthermore, a regularized extension of the scheme is given which can be used to limit the actuator signals.
The principle component least mean squares algorithm (PCLMS) is an elegant adaptive control algorithm for cancelling a tonal disturbance signal in active control applications, such as active noise control and active vibration isolation control. The algorithm removes the spatial correlation between the actuator inputs and the error sensor outputs to enable fast convergence of the adaptive controller. However, a drawback of the PCLMS algorithm is that it can only suppress a disturbance signal which contains a single frequency component. The contribution of this paper is that we present a numerically robust projection based approach in which the PCLMS is extended with the ability to suppress a disturbance signal which contains multiple frequency components. The potential of the algorithm is demonstrated by multi tonal control on a realistic model of a real-time vibration isolation set-up. The algorithm is shown to outperform the traditional filtered-x least mean squares algorithm.
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