Abstract
A modified model, in the form of an FIR filter, is proposed for the modelling of the acoustic dynamics of an active noise control system. This is a low order filter formulation but consists of two independent elements—a time delay and a d.c. gain. Empirical data has shown that this model constitutes a good representation of the equivalent high order FIR filter and has the additional feature of being a high frequency noise filtering device. Because of its specific structure, the time delay and gain must be identified independently. This restricts the use of the conventional least mean squares technique for parameter optimization, as the cost function intrinsically comprises multimodal error surfaces. The use of Genetic Algorithms could be the best solution to address this issue but their unpredictable response in real-time require some special attention. A fully developed active noise control system, based on the Genetic Algorithm, to achieve the objective of noise reduction is described. To further guarantee the reliability of this approach, a supervisory scheme is incorporated for governing the real-time learning operations. A parallel hardware architecture, using two independent TMS320C30 digital signal processors, is designed for such implementation. The experimental results indicate that this approach to noise control is sound, and that noise reduction of more than 15dB(A) is consistently obtained.
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Tang, K.S., Man, K.F., Kwong, S. et al. Application of the genetic algorithm to real-time active noise control. Real-Time Systems 11, 289–302 (1996). https://doi.org/10.1007/BF00337683
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DOI: https://doi.org/10.1007/BF00337683