基於FPGA之硬體實現T-S模糊小腦模型控制應用於音量控制

FPGA Implementation of Takagi-Sugeno Fuzzy Cerebellar Model Articulation Volume Control

陳彥融(Yen-Jung Chen)

指導教授 ： 劉寅春

淡江大學/工學院/電機工程學系碩士班/碩士(2014年)

https://doi.org/10.6846/TKU.2014.01167

摘要

在本篇論文中，我們使用T-S模糊小腦模型控制器去追蹤控制因環境噪音而需要調整的音量大小。此控制器有下列幾項優點：
1. 利用LMI求出控制增益，使CMAC初始權重提升了準確性。
2. 基於LMI設計具有自適應能力的CMAC，允許時變參數在系統中。
3. 控制器能夠快速並且反覆的修正控制量。
最後在實驗階段，以FPGA做為實現的平台。將T-S模糊小腦模型控制器實現在FPGA上，並且應於蜂鳴器的音量控制。從實驗結果可知，系統表現良好的追蹤效能。

關鍵字

T-S模糊 ； 線性矩陣不等式 ； 小腦模型控制器 ； 現場可編程邏輯陣列 ； 音量控制

並列摘要

In this study, we use Takagi-Sugeno fuzzy cerebellar model articulation controller (T-S CMAC) for tracking volume which is need to adjusted due to environmental noise. This controller has the following advantages:
1. Using linear matrix inequalities (LMI) to calculate the control gain, it improves the accuracy which is CMAC of the initial weights.
2. In order to track the time-varying parameter in CMAC, we designed the controller via LMI which has strong adaptive ability.
3. It can quickly and repeatedly correction amount of control.
Finally, this study will use the field-programmable gate array (FPGA) to implement T-S CMAC algorithm in experiment. It will apply to adjust volume. In experiment results, we can see the tracking ability is well.

並列關鍵字

T-S fuzzy ； Linear Matrix Inequalities (LMIs) ； Field-programmable gate array (FPGA) ； CMAC ； Volume control

參考文獻

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