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研究生:林隆貴
研究生(外文):Long-GueiLin
論文名稱:適用於具有直接傳輸項之未知非線性隨機混合系統之修正型NARMAX主動容錯狀態空間自調式追蹤器
論文名稱(外文):A New Active Fault Tolerance Tracker Using Modified NARMAX Model for State-Space Self-Tuning Control of Unknown Nonlinear Stochastic Hybrid Systems with a Direct Transmission Term
指導教授:蔡聖鴻
指導教授(外文):Jason Sheng-Hong Tsai
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:65
中文關鍵詞:狀態空間自調式控制隨機混合系統非線性自迴歸移動平均模型容錯控制觀測/卡爾曼濾波器鑑別直接傳輸矩陣
外文關鍵詞:Self-tuning controlstochastic systemNARMAX modelfault tolerant controlobserver/Kalman filter identificationdirect transmission matrix
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本論文提出適用於ㄧ考慮輸入影響輸出之直接傳輸項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。因此,這方法可以改善用於系統回復的參數估測,並且有效地處理局部突發式或逐步式錯誤的系統錯誤、以及突發式或逐步式的輸入錯誤。
An active fault tolerance tracker using the modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuning is proposed in this thesis for unknown nonlinear stochastic hybrid systems with a direct transmission matrix from input to output. Through observer/Kalman filter identification method, one has a good initial guess of NARMAX model to reduce the identification process time. Then, based on the NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown nonlinear system with a direct transmission matrix which have measurement noises and inaccessible system states. Besides, an effective fault tolerance scheme is proposed for unknown multivariable stochastic systems by modifying the conventional state-space self-tuning control approach for the detection of fault occurrence. 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 is developed by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm to improve the parameter estimation for faulty system recovery. Consequently, the proposed method can effectively cope with partially abrupt and/or gradual system faults and input failures by the proposed fault detection.
Chapter
1. Introduction............1
2. Modified NARMAX Model for Self-Tuning Control Scheme............3
2.1 The Structure of the State-Space STC.........................4
2.2 Modified NARMAX Model for Self-Tuning Control Scheme............5
3. Modified NARMAX Model-Based State-Space Observer for Self-Tuning Control............7
3.1 Preliminary Structure of Discrete-Time State-Space Observer............8
3.2 OKID Formulation............12
3.2.1 Basic observer equation............12
3.2.2 Computation of Markov parameters............14
3.2.3 Relationship to a Kalman filter............16
3.3 The Method of Optimal Linearization............17
3.4 STC Scheme of Unknown Nonlinear Stochastic Hybrid Systems Based on a Modified NARMAX Model with Initial OKID-Estimated Parameters............21
3.4.1 The on-line identification and observer based on modified NARMAX model through optimal linearization............21
3.4.2 The initial parameters of NARMAX model based on OKID............26
4 The Digital Tracker for the Sampled-Data Linear System with a Direct Transmission Term............29
5 The Modified NARMAX Model-Based State-Space Self-Tuning Control for The Unknown Nonlinear Stochastic Hybrid System with a Direct Transmission Term............34
6 Self-Tuning Control with Fault Tolerance............39
6.1 Problem Statement............40
6.2 Modified Active Fault Tolerance............41
7 An Illustrative Example............47
7.1 System Identification by Using RELS Method............47
7.1.1 Nonlinear NARMAX model system............47
7.1.2 The initial parameter of the modified NARMAX model............49
7.2 Active Fault Tolerance Using Modified NARMAX Model-Based State-Space Self-Tuning Control............53
8 Conclusion............62
Reference............63
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