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系統識別號 U0026-2207201311031800
論文名稱(中文) 適用於具有直接傳輸項和輸入飽和限制之未知非線性隨機混和系統的修正型NARMAX主動容錯狀態空間自調式追蹤器
論文名稱(英文) A Modified NARMAX Model-Based Self-Tuner with Fault Tolerance for Unknown Nonlinear Stochastic Hybrid Systems with an Input-Output Direct Feed-Through Term and Input Constraint
校院名稱 成功大學
系所名稱(中) 電機工程學系碩博士班
系所名稱(英) Department of Electrical Engineering
學年度 101
學期 2
出版年 102
研究生(中文) 林柏翰
研究生(英文) Po-Han Lin
學號 N26001165
學位類別 碩士
語文別 英文
論文頁數 86頁
口試委員 指導教授-蔡聖鴻
口試委員-郭淑美
口試委員-蔡宗吉
口試委員-林明宏
口試委員-杜彥頤
中文關鍵字 狀態空間自調式控制;隨機混和系統;非線性自迴歸移動平均模型;容錯控制;觀測/卡爾曼濾波器鑑別;最小平方遞歸;直接傳輸矩陣;輸入飽和限制。 
英文關鍵字 Self-tuning control  stochastic system  NARMAX model  fault tolerant control  OKID  RELS  system identification  direct transmission matrix  input constraint 
學科別分類
中文摘要 本論文提出適用於ㄧ考慮輸入影響輸出之直接傳輸項和輸入飽和限制的未知非線性隨機混合系統,以修正型非線性自迴歸移動平均模型為基底的主動容錯型狀態自調式控制方法。利用觀測/卡曼濾波器鑑別,可以得到一個優良的初始非線性自迴歸移動平均模型,並且可以縮短鑑別系統的時間。然後,基於修正型非線性自迴歸移動平均模型之系統鑑別,一個相對應的適應性數位控制法則被提出以適用於狀態不可測之考慮直接傳輸項的未知非線性隨機系統。此外,將自調式控制方法加以修改,發展出一種對未知多變數隨機系統的容錯控制法。當受控系統發生故障時,藉著比較在卡曼濾波器估測演算法中的誤差值,一種量化的準則被發展出來:權重矩陣重新設定技術,它是藉著調整和重新設定在卡曼濾波器估測演算法中用以估測參數的協方差矩陣。最後,運用具有輸入限制與狀態延遲的大尺度未知連結系統,且配合反飽和機制以建構分散式軌機追蹤器。在本論文中,以例題來說明所提方法之有效性。
英文摘要 A modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuner with fault tolerance is proposed in this thesis for unknown nonlinear stochastic hybrid systems with a direct transmission matrix from input to output. Through the off-line observer/Kalman filter identification method, one has a good initial guess of modified NARMAX model to reduce the on-line system identification process time. Then, based on the modified NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown continuous-time nonlinear system, with an input-output direct transmission term, which also has measurement and system noises and inaccessible system states. Besides, an effective state space self-turner with fault tolerance scheme is presented for the unknown multivariable stochastic system. A quantitative criterion is suggested by comparing the innovation process error estimated by the Kalman filter estimation algorithm, so that a weighting matrix resetting technique by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm is utilized to achieve the parameter estimation for faulty system recovery. Finally, applying the proposed anti-windup scheme according to these models establishes decentralized trajectory trackers for unknown interconnected large-scale systems with input constraints and state delay. The illustrative example is given to demonstrate the effectiveness of the proposed method.
論文目次 Chapter
1.Introduction...1
2.Problem Description and Movitation...6
3.Preliminary...11
3.1 RELS-based observer/Kalman filter in state-space innovation form...12
3.2 OKID–based observer/Kalman filter in general coordinate form...16
3.3 Optimal linearization...18
4.Modified NARMAX Model-Based State-Space Self-Tuning Control for Unknown Nonlinear Stochastic Hybrid Systems with Input-output Direct Feed-Through Term...22
4.1The initial parameters of NARMAX model based on OKID...27
4.2 The digital tracker for sampled-data linear system with a direct transmission term...29
5.Self-Tuning Control with Fault Tolerance...34
6.A new Anti-wind Tracker for MIMO systems under Input constraint with a Feed Through Term...41
6.1 Motivation of the modified optimal control...42
6.2 Characteristic of the modified control scheme...47
6.3 Digital redesign of optimal control for sample-data systems under input constraint...53
7.An Illustrative Example...62
7.1 Control of the nonlinear NARMAX model system...62
7.2 Active fault tolerance using modified NARMAX model-based
state-space self-tuning control without input constraint...64
7.3 Active fault tolerance using modified NARMAX model-based
state-space self-tuning control with input constraint...66
8.Conclusion...69
References...70
Appendix A...76
Appendix B...80
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