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研究生: 龔聖賢
Gong, Sheng-Sian
論文名稱: 以數位訊號處理器實現智慧型氣動馬達速度控制系統
Realization of Intelligent Air Motor Speed Control System via Digital Signal Processor
指導教授: 陳瑄易
Chen, Syuan-Yi
學位類別: 碩士
Master
系所名稱: 電機工程學系
Department of Electrical Engineering
論文出版年: 2015
畢業學年度: 103
語文別: 中文
論文頁數: 82
中文關鍵詞: 氣動馬達速度控制數位訊號處理器滑動模式控制類神經網路
英文關鍵詞: air motor, speed control, digital signal processor, sliding-mode control, neural network
論文種類: 學術論文
相關次數: 點閱:110下載:4
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  • 本論文之目標為設計智慧型控制系統以對葉片式氣動馬達伺服系統進行速度控制。在論文中,首先對氣動馬達之構造及運作原理進行討論,接著分析氣動馬達系統之數學動態,以推導出氣動馬達之標準二階狀態方程式。由於氣動馬達之動態特性及系統參數為高度非線性且時變,為了在既有的非線性特性及外部擾動情況下仍能達到高精準度之速度控制,本論文提出了基於比例積分微分型模糊類神經網路與適應性動態滑動模式兩種智慧型控制系統作為氣動馬達之速度控制器。最後,本論文以具32位元浮點數運算能力之數位訊號處理器TMS320F28335實現所提出的控制系統。實驗結果顯示以本論文所提出之兩種智慧型控制系統對氣動馬達均能達到有效之速度控制。

    The object of this study is to design intelligent control systems for controlling the speed of a vane-type air motor (VAM) pneumatic servo system for tracking reference speed command. First, the structure and operating principles of the VAM servo system are introduced. Then, the dynamics of the VAM servo system is analyzed to derive the second order state equation of the VAM. Moreover, due to the dynamic characteristics and system parameters of the VAM servo system are highly nonlinear and time-varying, intelligent controllers control systems including proportional-integral-derivative-based fuzzy neural network (PID-based FNN) and adaptive dynamic sliding-mode control (ADSMC), are proposed to achieve precise speed control of VAM servo system under the occurrences of the inherent nonlinearities and external disturbances. Finally, a 32-b floating-point digital signal processor (DSP) TMS320F28335 was adopted for implementing the proposed control systems. The experimental results demonstrated the validities and advantages of the proposed PID-based FNN and ADSMC systems for the VAM servo system.

    摘 要 I ABSTRACT II 目錄 IV 圖目錄 VI 表目錄 IX 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 3 1.3 研究方法 4 1.4 研究架構 8 第二章 氣動馬達實驗平台介紹 9 2.1 氣動馬達的結構與運作原理 9 2.2 氣動馬達動態分析 12 2.3 實驗平台設計 16 2.4 實驗平台設置 30 2.5 數位訊號處理器軟體規劃 31 第三章 基於比例積分微分類神經網路之氣動馬達速度控制系統 33 3.1 前言 33 3.2比例積分微分類神經網路速度控制器 33 3.2.1 比例積分微分類神經網路架構 34 3.2.2 比例積分微分類神經網路線上學習演算法 36 3.2.3 比例積分微分類神經網路收斂性分析 38 3.3 比例積分微分類神經網路氣動馬達速度控制系統 40 3.4 實驗結果 41 第四章 基於比例積分微分型模糊類神經網路之氣動馬達速度控制系統 47 4.1 前言 47 4.2比例積分微分型模糊類神經網路速度控制器 48 4.2.1比例積分微分型模糊類神經網路架構 48 4.2.2 比例積分微分型模糊類神經網路線上學習演算法 50 4.2.3 比例積分微分型模糊類神經收斂性分析 52 4.3 比例積分微分型模糊類神經網路氣動馬達速度控制系統 55 4.4 實驗結果 56 第五章 基於適應性動態滑動模式之氣動馬達速度控制系統 59 5.1 前言 59 5.2 適應性動態滑動模式速度控制器 60 5.2.1 滑動模式控制架構 60 5.2.2 動態滑動模式控制架構 61 5.2.3 適應性動態滑動模式控制架構 62 5.3 適應性動態滑動模式氣動馬達速度控制系統 67 5.4 實驗結果 69 第六章 結論與未來工作 74 6.1結論 74 6.2未來工作 76 參考文獻 77 系統參數表 81

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