Introduction
The technique of active noise cancellation (ANC), which uses artificial signals to cancel undesired noise, has received much attention during the past decade because of recent advances in electronics and microcomputers. Conventional methods, called passive noise control (PNC), have the ability to suppress the higher frequency acoustic noise rather than the lower frequency noise, as proven by several researchers in many papers. However, industrial acoustic noise often has its main power on lower frequencies,where the wavelength of sound is so long that passive techniques are no longer cost-effective because they require material that is too bulky and heavy, such as the silencer of a car.In contrast to passive methods, active methods not only permit the cancellation of lower frequency noise, but also reduce the weight, volume and cost of the overall noise control system.
To put in place an ANC system, one has to identify some transfer functions of acoustic plants and transducers, such as microphone, speaker and duct plant, to generate the correct anti-noise signal. Many researchers have employed the filtered-X algorithm as an active noise controller because of its simplicity. The filtered-X algorithm is also an adaptive filter and its weighting parameters can be automatically updated by the least mean square (LMS) algorithm. This approach is effective at attenuating lower frequency noise, such as that from a fan, compressor, or engine noise in an acoustic duct. However, there are still several problems with the filtered-X strategy. One of the most critical disadvantages of the filtered-X LMS algorithm is the low convergence speed. This is because the filtered-X algorithm needs a small step gain to update the weighting parameters in order to maintain stable performance of the system. A concurrent difficulty is that small step gains cannot update the weights in time to keep up with the change of residual noise, which plays a leading role in the filtered-X algorithm. Hence, the tracking speed is very slow and an accurate anti-signal cannot be derived to cancel the undesired noise.
These decrease the performance of broadband noise reduction.A few ANC systems based on fuzzy logic have been proposed, most of which use FIR filters or PD controllers to suppress noise, and then use a fuzzy method to adapt the system parameters. However, these approaches still require mathematical information about the duct plant, and they are still very complex.Rather than another fuzzy ANC system, this project proposes a fuzzy adaptive filtered-X algorithm to enhance the performance of ANC systems.The fuzzy filtered-X algorithm is mainly composed of linguistic information from human experts, and only a little numerical information is needed. Moreover, this method can minimise the residual noise of a fuzzy adaptive ANC system by properly setting the initial weighting parameters. Hence, the critical problem of low convergence speed is overcome and the residual noise can be minimised. Compared with other ANC schemes, the proposed fuzzy approach provides a very easy way to develop an active noise controller. In addition, the proposed fuzzy adaptive system can be used for many other applications.
The technique of active noise cancellation (ANC), which uses artificial signals to cancel undesired noise, has received much attention during the past decade because of recent advances in electronics and microcomputers. Conventional methods, called passive noise control (PNC), have the ability to suppress the higher frequency acoustic noise rather than the lower frequency noise, as proven by several researchers in many papers. However, industrial acoustic noise often has its main power on lower frequencies,where the wavelength of sound is so long that passive techniques are no longer cost-effective because they require material that is too bulky and heavy, such as the silencer of a car.In contrast to passive methods, active methods not only permit the cancellation of lower frequency noise, but also reduce the weight, volume and cost of the overall noise control system.
To put in place an ANC system, one has to identify some transfer functions of acoustic plants and transducers, such as microphone, speaker and duct plant, to generate the correct anti-noise signal. Many researchers have employed the filtered-X algorithm as an active noise controller because of its simplicity. The filtered-X algorithm is also an adaptive filter and its weighting parameters can be automatically updated by the least mean square (LMS) algorithm. This approach is effective at attenuating lower frequency noise, such as that from a fan, compressor, or engine noise in an acoustic duct. However, there are still several problems with the filtered-X strategy. One of the most critical disadvantages of the filtered-X LMS algorithm is the low convergence speed. This is because the filtered-X algorithm needs a small step gain to update the weighting parameters in order to maintain stable performance of the system. A concurrent difficulty is that small step gains cannot update the weights in time to keep up with the change of residual noise, which plays a leading role in the filtered-X algorithm. Hence, the tracking speed is very slow and an accurate anti-signal cannot be derived to cancel the undesired noise.
These decrease the performance of broadband noise reduction.A few ANC systems based on fuzzy logic have been proposed, most of which use FIR filters or PD controllers to suppress noise, and then use a fuzzy method to adapt the system parameters. However, these approaches still require mathematical information about the duct plant, and they are still very complex.Rather than another fuzzy ANC system, this project proposes a fuzzy adaptive filtered-X algorithm to enhance the performance of ANC systems.The fuzzy filtered-X algorithm is mainly composed of linguistic information from human experts, and only a little numerical information is needed. Moreover, this method can minimise the residual noise of a fuzzy adaptive ANC system by properly setting the initial weighting parameters. Hence, the critical problem of low convergence speed is overcome and the residual noise can be minimised. Compared with other ANC schemes, the proposed fuzzy approach provides a very easy way to develop an active noise controller. In addition, the proposed fuzzy adaptive system can be used for many other applications.
No comments:
Post a Comment