Active Noise Control Based on Fuzzy Models

This paper presents a new approach to acoustic noise control, by introducing a fuzzy model-based control strategy. Classical linear identification and control tools have been applied to active noise control in the last two decades. In this type of control, the limitations of their applicability are well defined. Therefore, new techniques must be developed in order to increase the performance of active noise controllers. Recent research in control has emphasized the importance of nonlinear model-based control, increasing the performance of several types of systems. From the different nonlinear techniques, fuzzy modeling is one of the most appealing. Fuzzy clustering can be used to identify models directly from data. These models proved to be accurate for complex and partly known systems, and can represent highly nonlinear systems in an effective way due to their function approximation properties. This paper applied fuzzy and classical modeling to real-time noise data. The fuzzy model revealed to be more accurate than the linear model. An inverse model is derived which is applied in the proposed modelbased control scheme. The paper presents control results derived from an experimental setup, revealing best performance than classical control methods.