| 研究生: |
李至浩 Lee, Chih-Hao |
|---|---|
| 論文名稱: |
適用於含直接傳輸項之未知系統並具輸入飽和限制功能的新型觀測器及追蹤器:數位重新設計 A New Linear Quadratic Observer and Tracker under Input Constraint for the Unknown System with a Direct Feedthrough Term: Digital Redesign Approach |
| 指導教授: |
蔡聖鴻
Tsai, S. H. Jason |
| 學位類別: |
碩士 Master |
| 系所名稱: |
電機資訊學院 - 電機工程學系 Department of Electrical Engineering |
| 論文出版年: | 2013 |
| 畢業學年度: | 101 |
| 語文別: | 英文 |
| 論文頁數: | 43 |
| 中文關鍵詞: | 觀測/卡爾曼濾波器鑑別方法 、觀測型追蹤器 、數位重新設計 、輸入飽和限制 |
| 外文關鍵詞: | Observer/Kalman filter identification, observer-based tracker, digital redesign, input constraint |
| 相關次數: | 點閱:109 下載:0 |
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本論文針對含有直接傳輸項之未知線性系統提出新型的線性二次觀測器與追蹤器,本文利用觀測/卡爾曼濾波器鑑別方法計算出具有直接傳輸項之資料取樣線性模型的適當階數的線性觀測器。再根據此模型,開發出具有高增益特性及最佳化之線性二次型類比觀測型追蹤器,使之有效率的獲得高品質狀態估測以及追蹤軌跡。此外,在含有直接傳輸項之線性系統下,採用具有預估特性之數位重新設計方法將具有高增益類比觀測器與追蹤器轉換得到一低增益且可執行的數位觀測器與追蹤器。為了降低控制力來滿足輸入飽和限制的需要,提出修正型的線性二次式數位軌跡追蹤器。藉此控制力可以有效地被壓縮,而且不會損失原本良好的軌跡追蹤效果。
This thesis develops a new linear quadratic observer and tracker for the linear unknown system with a direct transmission term from input to output. The off-line observer/Kalman filter identification (OKID) method is used to determine the sampled-data linear model with a direct feedthrough term. Based on this model, a high-gain optimal linear quadratic analogue observer-based tracker are proposed, so that it can effectively induce a high quality performance on the state estimation and the trajectory tracking design. Besides, the prediction-based digital redesign method is utilized to obtain a relatively low-gain and implementable observer and digital tracker from the theoretically well-designed high-gain analogue observer and tracker for the linear system with a direct transmission term from input to output. To reduce the magnitude of control input, which is caused by the high-gain property to fit the requirement of the input constraint, the modified linear quadratic analogue tracker (LQAT) is proposed. Thus, the control input can be compressed effectively without losing the original high performance of tracking much.
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校內:2023-12-31公開