利用遞迴式小波模糊類神經網路於電動轉向系統定位控制之研究

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孫一イ凡(I-Fan Sun) 查詢紙本館藏

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電機工程學系

論文名稱

利用遞迴式小波模糊類神經網路於電動轉向系統定位控制之研究
(A Study of Position Control on Electrical Power Steering System using Recurrent Wavelet Fuzzy Neural Network)

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摘要(中)

本文之研究目的為研製以數位訊號處理器為基礎之遞迴式小波模糊類神經網路之智慧型滑動模態控制，並應用於一基於六相永磁同步馬達之電動轉向系統，以期能改進其追隨效能與載具穩定性。首先，本研究將利用拉格朗日力學推導電動轉向系統的動態數學方程式。接著，以此方程式為基礎，設計一滑動模態控制器作為電動轉向系統之位置控制器。由於電動轉向系統是一個非線性與時變的系統，控制的準確性將深受整個電動轉向
系統的參數變化、外力干擾與摩擦力…等不確定項的影響。因此在實際應
用上，不確定項的上界難以精確取得，而間接導致滑動模態控制成效容易
趨於保守，也容易造成其控制抖動之缺陷。有鑒於此，本研究提出了具有
遞迴式小波模糊類神經網路作為不確定項估測器的智慧型滑動模態控制策
略來改善此現象。其中，遞迴式小波模糊類神經網路能適時地估測當下因
參數變化與外來干擾所產生之不確定項的大小，並加入強健控制器來消除
近似誤差。其適應性線上學習法則則是藉由李亞普諾夫理論與泰勒展開式
推導而得。最後，本研究以32 位元浮點運算數位訊號處理器完成所提出的電動轉向定位系統，且利用實驗結果來驗證所提出智慧型控制策略於電動轉向系統之定位成效與可行性。

摘要(英)

The objective of this thesis is to develop a digital signal processor (DSP) based intelligent control of a six-phase permanent magnet synchronous motor (PMSM) drive system for electric power steering (EPS) system. First of all, the
dynamic mathematical model of the EPS system is derived by Lagrangian dynamics. Since the EPS system is a nonlinear and time-varying system, the control accuracy is very sensitive to the parameter variations and external disturbances. Therefore, a sliding mode controller (SMC) is developed for the position control of the EPS system. However, the upper bound of the uncertainties is difficult to obtain in advance and the choice of high switching control gain in SMC may cause undesired chattering phenomenon. Hence, an intelligent SMC with a recurrent wavelet fuzzy neural network (ISMC-RWFNN) is proposed in this study, where the RWFNN is adopted as an uncertainty estimator to overcome the aforementioned disadvantage of SMC. Also, a robust compensator is employed to eliminate the estimation error. Furthermore, adaptive learning algorithms for the online training of the RWFNN are derived using the Lyapunov theorem and Taylor series. Finally, the proposed ISMC-RWFNN to control the six-phase PMSM drive system for the EPS system is implemented in a 32-bit floating-point digital signal processor (DSP), TMS320F28335, and the effectiveness is verified by some experiments.

關鍵字(中)

★ 六相永磁同步馬達
★ 電動轉向系統
★ 小波轉換
★ 遞迴式模糊類神經網路
★ 滑動模態控制器
★ 李亞普諾夫穩定性

關鍵字(英)

★ Six-phase permanent synchronous motor
★ electric power steering system
★ wavelet transform
★ recurrent fuzzy neural network
★ sliding-mode controller
★ Lyapunov stability

論文目次

中文摘要 I
英文摘要 II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 IX
第一章　緒論 1
1.1 研究動機與目的 1
1.2 文獻回顧 2
1.3 論文大綱 6
1.4 論文貢獻 7
第二章六相永磁同步馬達驅動系統之週邊硬體介紹 8
2.1 前言 8
2.2 TMS320F28335數位訊號處理器簡介 9
2.3 TMS320F28335週邊功能介紹 12
2.3.1 脈波寬度調變模組 12
2.3.2 中斷訊號 14
2.3.3 類比/數位轉換模組 15
2.3.4 正交編碼器脈衝模組 16
2.3.5 串列周邊介面模組 18
2.4 以DSP為基礎的六相永磁同步馬達控制系統 20
2.4.1 TMS320F28335控制卡 20
2.4.2 TMS320F28335介面板 21
2.4.3 週邊電路擴充控制板 22
2.5 週邊擴充控制板之電路 23
2.5.1 ADC電壓準位轉換電路 23
2.5.2 脈波寬度調變電壓準位轉換電路 24
2.5.3 過電流保護電路 25
2.5.4 數位/類比轉換電路 25
2.5.5 編碼器之解碼電路 25
第三章六相永磁同步馬達驅動系統 28
3.1 前言 28
3.2 六相永磁同步馬達 30
3.3 六相永磁同步馬達數學動態模型 30
3.4 座標轉換之電壓及電磁轉矩方程式 32
3.5 空間向量脈波寬度調變 35
3.6 六相永磁同步馬達控制架構 47
第四章基於六相永磁同步馬達之電動轉向系統 50
4.1 前言 50
4.2 電動轉向系統 50
4.3 拉格朗日力學之背景簡介 52
4.4 電動轉向系統之數學動態模型推導 53
4.5 滑動模態控制器 56
4.6 實驗結果與討論 61
第五章以遞迴式小波模糊類神經網路控制器來估測滑動模態控制力之不確定項 68
5.1 前言 68
5.2 類神經網路與模糊邏輯 69
5.3小波轉換 72
5.4遞迴結構 74
5.5 從滑動模態控制到智慧型網路 75
5.6 遞迴式小波模糊類神經網路架構 77
5.7 結合遞迴式小波模糊類神經網路估測器之智慧型滑動模態控制的線上學習法則與穩定性分析 81
5.8 實驗結果與討論 88
第六章結論與未來展望 93
6.1 結論 93
6.2 未來展望 95
附錄 96
李亞普諾夫方程式之P與Q求解流程 96
參考文獻 97
作者簡歷 104

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指導教授

林法正(Faa-Jeng Lin)

審核日期

2015-8-24

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