A novel design strategy for Iterative learning control based on approximate fuzzy data model (AFDM) for active noise control: Theoretical background

摘要

Noise pollution from modern industrial activities is an environmental problem of growing importance. At the moment, these noise problems are mostly addressed in a passive way (e.g. Encapsulations, absorbing material, etc.). Therefore, in certain applications, active solutions are a potential alternative, suitable for the reduction of low frequency noise. This paper describes the theoretical application of a novel design strategy for iterative learning control based on approximate fuzzy data model (AFDM) for active noise control. The conventional ILC methods have to star its learning with zero initial input assumption. Instead of such zero initial input assumption, in this paper, the idea of using the past noise experiences on the initial input selection for new noise cancellations have been highlighted. It is shown that the use of past experiences can result in satisfactory reduction of the number of initial iterations, improving the efficiency computational.