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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 楊士進 | zh_TW |
| dc.contributor.advisor | Shih-Chin Yang | en |
| dc.contributor.author | 徐暐傑 | zh_TW |
| dc.contributor.author | Wei-Chieh Hsu | en |
| dc.date.accessioned | 2023-08-15T17:29:53Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-15 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-05 | - |
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Zigliotto, "Sensorless full-digital PMSM drive with EKF estimation of speed and rotor position," IEEE Transactions on Industrial Electronics, vol. 46, no. 1, pp. 184-191, 1999, doi: 10.1109/41.744410. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88718 | - |
| dc.description.abstract | 本論文針對應用編碼器角度感測器進行永磁馬達磁場導向速度控制以及擾動解耦控制策略，提出系統轉動慣量的在線估測方法，其應用是在工業控制器或是工廠自動搬運車中，確保能將精確的慣量用作速度控制器的設計以及進行系統負載轉矩的準確估測，以改善速度控制的動態響應。
本論文提出的在線慣量估測架構整合無跡卡爾曼濾波器(Unscented Kalman Filter-UKF)與變遺忘因子最小遞歸二乘法(Variable Forgetting Factor Recursive Least Square-VFFRLS)，先透過UKF對馬達的轉速以及負載轉矩進行估測，估測的轉速以及負載轉矩將用於VFFRLS的算法當中進行慣量的估測，能有效解決RLS算法容易受到輸入訊號雜訊干擾的問題。此外，估測的慣量會代回UKF的馬達模型中，藉此降低UKF模型中慣量的誤差，以得到準確的狀態估測結果。 最後本論文針對一顆470W用於自動搬運車應用的永磁馬達進行實驗測試，根據實驗結果，整體慣量估測的誤差與實際慣量相比能被限縮在2%以內，而在0到100%額定轉速的梯形波速度命令下，自適應控制下的響應上升時間為550ms、安定時間為890ms，與傳統固定增益的速度控制器比較，暫態上升時間減少5.6%、安定時間減少11.4%，能有效改善速度控制的動態響應性能。 | zh_TW |
| dc.description.abstract | This thesis proposes an online inertia estimation method specifically tailored to the application of encoder angle sensors for magnetic field-oriented velocity control and disturbance decoupling strategy in permanent magnet motors. The application of this method is intended for industrial controllers or factory automated guided vehicles(AGV), aiming to ensure precise estimated inertia are used for speed controller design and accurate estimation of load torque. This, in turn, improves the dynamic response of the velocity control.
The online inertia estimation framework presented in this thesis integrates the Unscented Kalman Filter (UKF) and the Variable Forgetting Factor Recursive Least Square (VFFRLS) algorithms. The UKF is employed to estimate the motor's speed and load torque, and the estimated speed and load torque are then used in the VFFRLS algorithm to perform inertia estimation, effectively addressing the issue of RLS algorithm susceptibility to input signal noise interference. Furthermore, the estimated inertia is fed back into the UKF motor model to reduce the inertia estimation errors, resulting in more accurate state estimation outcomes. Finally, a 470W motor used in AGV applications is tested. According to the experimental results, the overall inertia estimation error can be constrained within 2%. Under a trapezoidal speed command ranging from 0% to 100% of the rated speed. The rising time under adaptive control is 550ms, and the settling time is 890ms, When compared to the conventional fixed-gain speed controller, the transient rising time is reduced by 5.6% and the settling time is reduced by 11.4%. These findings suggest that our proposed method can effectively optimize the dynamic response performance of speed control. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-15T17:29:53Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-15T17:29:53Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
中文摘要 iii ABSTRACT v 目錄 vii 表目錄 x 圖目錄 xii 符號列表 xv 第1章 緒論 1 1.1 研究背景 1 1.2 文獻回顧 2 1.2.1 永磁馬達慣量識別方法 2 1.2.2 混合擾動觀測器與梯度下降法進行擾動解耦和慣量識別 7 1.2.3 數值方法進行擾動解耦和慣量識別 11 1.2.4 改進RLS慣量估測性能之輸入訊號前處理方法 15 1.3 研究目的 19 1.3.1 永磁馬達轉動慣量與負載轉矩識別性能改善 20 1.3.2 轉速自適應控制和擾動解耦的性能改善 20 1.4 論文大綱 22 第2章 自適應控制與擾動解耦 23 2.1 基於無跡卡爾曼濾波器之狀態估測 24 2.1.1 轉速與負載轉矩估測 24 2.1.2 慣量估測輸入訊號之訊號篩選 29 2.2 變遺忘因子RLS慣量識別 31 2.2.1 轉動慣量估測 31 2.2.2 變遺忘因子算法 32 2.2.3 慣量估測條件與初始化參數 34 2.3 自適應控制與擾動解耦控制策略 35 2.3.1 自適應控制策略 35 2.3.2 擾動解耦策略 37 第3章 自適應控制與擾動解耦實驗結果 39 3.1 自適應控制於無載條件下有效性驗證 39 3.1.1 無載測試實驗平台 39 3.1.2 阻尼係數對於速度控制敏感度測試 41 3.1.3 變遺忘因子RLS實驗結果 42 3.1.4 動態調整UKF Q矩陣實驗結果 45 3.1.5 UKF與傳統卡爾曼濾波器性能比較 47 3.1.6 自適應控制動態性能驗證 49 3.1.7 慣量估測於無載條件下實驗總結 54 3.2 自適應控制於加載條件下有效性驗證 56 3.2.1 加載測試實驗平台 56 3.2.2 UKF-VFFRLS在加載條件下慣量估測測試 57 3.2.3 擾動解耦於等速條件下性能測試 60 3.2.4 自適應控制與擾動解耦綜合性能測試 63 3.2.5 慣量估測於加載條件下實驗總結 70 第4章 結論及未來工作 74 4.1 結論 74 4.1.1 自適應控制 74 4.1.2 擾動解耦 75 4.2 未來工作 75 4.2.1 高負載條件下的慣量估測 75 4.2.2 無感測器自適應控制 76 參考文獻 77 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 擾動估測 | zh_TW |
| dc.subject | 慣量估測 | zh_TW |
| dc.subject | 自適應控制 | zh_TW |
| dc.subject | 擾動解耦 | zh_TW |
| dc.subject | Disturbance decoupling | en |
| dc.subject | Adaptive control | en |
| dc.subject | Disturbance estimation | en |
| dc.subject | Inertia estimation | en |
| dc.title | 利用伺服馬達慣量估測進行控制器性能改善 | zh_TW |
| dc.title | Control Performance Improvement for Servo Motor Using Inertia Identification | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 陳耀銘;劉添華;李宇修 | zh_TW |
| dc.contributor.oralexamcommittee | Yaow-Ming Chen;Tian-Hua Liu;Yu-Hsiu Lee | en |
| dc.subject.keyword | 自適應控制,擾動解耦,慣量估測,擾動估測, | zh_TW |
| dc.subject.keyword | Adaptive control,Disturbance decoupling,Inertia estimation,Disturbance estimation, | en |
| dc.relation.page | 81 | - |
| dc.identifier.doi | 10.6342/NTU202303110 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-08-08 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 機械工程學系 | - |
| Appears in Collections: | 機械工程學系 | |
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| File | Size | Format | |
|---|---|---|---|
| ntu-111-2.pdf Restricted Access | 6.98 MB | Adobe PDF |
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