Fuzzy active noise modeling and control
The design of active noise control (ANC) has been developed in the. last two decades based on linear identification and control tools. However, acoustic processes present nonlinearities coming both from the characteristics of the actuator and from the nature of the process. Recent research has emphasized the importance of nonlinear model-based controllers, which increase the performance of several types of systems. From the different nonlinear techniques, fuzzy modeling is one of the most utilized. Direct and inverse multivariable fuzzy models can be identified directly from data using fuzzy clustering. Inverse models can then be applied directly as controllers, which can be included in an active noise control scheme. This paper proposes the use of fuzzy techniques in ANC. The performance of the proposed control schemes is compared to classical finite impulse response ANC in an experimental setup. The proposed fuzzy control scheme outperforms classical active noise controllers. (C) 2002 Elsevier Science Inc. All rights reserved.
active noise control fuzzy modeling inverse control fuzzy systems