管道消音與平台隔振之主動控制

主動噪音控制(ANC)及主動振動控制(AVC)籍由適應性地調適第二控制點, 使第二控制點產生與主要聲源或振源大小相等、相位相反的干涉波，而得以實現噪音消除及振動抑制之目的。論文包含了以下二個部份: 論文第一部份運用了模糊類神經網路搭配誤差倒傳遞演算，控制第二聲源，以達到管道噪音消除的目的，模糊推理系統重要的優點在於結構化的知識，被表示為IF-THEN的條列式規則，但是此一模糊推理系統對應於外在的環境時，卻缺乏了自我調整的能力。將類神經網路與模糊系統結合，可以使得模糊推理系統擁有調適能力，進而建立模糊規則。籍由管道下游的麥克風可以量測衰減性能及控制誤差，控制結果對於單頻噪音有40dB的衰減量，而對於雙頻噪音則有30dB 的衰減量。論文第二部份, 包含了運用多層感知 (MLP)，幅射基底函數(RBF)，小腦模型 (CMAC)及模糊 (Fuzzy) 類神經網路，其結合了誤差倒傳遞演算法，以控制音圈致動器，進而消除隔振平台的振動。此外，對於隔振平台的三點及四點控制，則利用了簡單的控制方法，即適應性有限脈衝響應(FIR)控制法，以消除外來擾動的振動。過去對於控制振動的方法，通常都是由推導出物理模式的數學模型來加以設計。環顧本論文所使用的控制法，最重要的優點是控制器的參數具有自我調適的能力，而能適應控制環境的改變。由隔振平台上的加速規可量測振動消除性能及控制效率，實驗結果顯示，利用以上所採用的控制方法可以大幅消除隔振平台的共振振動及外來干擾的振動。

關鍵字

主動噪音控制；主動振動控制；類神經網路；模糊類神經網路；幅射基底類神經網路；小腦模型類神經網路

並列摘要

Base on the principle of the superposition of waves, active noise control (ANC) and active vibration control (AVC) is achieved by adaptively tuning a secondary source which produces an anti-noise of equal amplitude and opposite phase with primary source. This thesis includes two parts of study which are shown as below. The first part of this thesis presents the study on the acoustic attenuation in a duct by using the combination of fuzzy neural network (FNN) with error back propagation algorithm to control secondary source. The most important advantage of fuzzy inference system is that the structured knowledge is represented in the form of fuzzy IF-THEN rules. But it lacks the ability to accommodate the change of external environments. Combining neural network with fuzzy system can help in this tuning process by adapting fuzzy sets and creating fuzzy rules. The performance of attenuation and control error can be measured by the microphone placed in the downstream of duct. The results of this study show that the acoustic attenuation by 40dB for pure-tone noise and nearly 30dB for dual-tones noise are obtained. In the second part of this thesis, it presents the study on the vibration attenuation in an isolated platform by combining multi-layer perception (MLP) neural network, radial basis function (RBF) neural network, cerebella model articulation controller (CMAC) neural network and fuzzy neural networks (FNN) with error back propagation algorithm to control voice coil actuator. Besides, for 3-points and 4-points control for this isolated platform, a simple control method, the adaptive finite impulse response (FIR) control method is applied to attenuate vibration of external disturbance. Usually, the methods in past time to control vibration were mainly designed by using mathematical models, which must be nearly close to the actual plant models. As regards to these utilized control methods, the most important advantage of them are that they have capability of self tuning the parameters of controllers and could adapt the changes of the environments. The performance of attenuation and control effectiveness can be evaluated by placing the accelerator to measure the amplitude at the center of the isolated platform. The experimental results in this study show that the control methods as adopted could greatly attenuate the vibration of resonance and external disturbance in an isolation platform.

並列關鍵字

Active Noise Control ； Active Vibration Control ； Neural Network ； FNN ； RBF ； CMAC

參考文獻

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[6] J. Shaw, Active Vibration Isolation by Adaptive Control, Journal of Vibration and Control, 7(1), pp. 19-31, 2001.

[7] S. D. Snyder and Nobuo Tanaka, Active Control of Vibration Using a Neural Network, IEEE Transactions on Neural Networks, 6(4), 1995

[8] L. Benassi and S.J. Elliott, Active vibration isolation using an inertial actuator with local displacement feedback control, Journal of Sound and Vibration, 278, 2004.

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管道消音與平台隔振之主動控制

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