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標題: | 使用於強韌參數識別及時變參數識別之新演算法 New Algorithms for Robust Parameter Identification and Time-Variant Parameter Identification |
作者: | Jyun-Sian Li 李俊賢 |
指導教授: | 陳明新 |
關鍵字: | 強韌參數識別,時變參數識別,干擾識別,卡曼濾波器, robust identification,time-variant parameter identification,disturbance identification,Kalman filter, |
出版年 : | 2012 |
學位: | 博士 |
摘要: | 本論文主要在探討連續時間參數識別的兩大主題,一為遭受非隨機
干擾下的參數識別,另一為時變參數的識別。除了量側雜訊外,一般 系統的輸出通常會被干擾所汙染,而這些干擾包含感測器的誤差、模 型誤差以及系統遭受外部擾動而引發。大部分的系統識別方法僅考慮 干擾為白雜訊,當出現以上的干擾時會造成參數估側偏差。在作參數 化時,我們可以將不同來源的干擾總和於一個干擾於輸出端。在本論 文中, 我們將提出一個離線識別以及兩個線上及時識別的方法來處理 此問題。 在離線識別的方法中,未知的干擾將由一有限項的傅立葉餘弦級數 來表示。此級數係數為未知。藉由結合級數的基底函數及原本的回歸 向量(regressor) 可得到一組擴展的回歸向量,藉由最小平方法做批量計 算,即可得到包含係數及參數在內的估測值。最後並提出此擴展的回 歸向量於持續刺激性(persistent excitation)的必要條件。 在第一個線上估測方法中,其架構是建立在梯度演算法上。在干擾 的影響下,為了使參數的誤差方程式可以收斂到零,必須作額外的控 制補償。於控制設計上,將使用平均法來得到近似系統,並利用H1頻 率成型來合成控制器。此控制訊號將可追蹤干擾訊號並將之抵消,因 此可保證估測參數可收斂到正確值。 第二個線上估測的方法為使用狀態估測器。為了將干擾納入估測器 作估測,於此我們提出一種干擾產生濾波器,並將其模型加入原參數 狀態方程式中。利用卡曼濾波器(Kalman filter)作狀態估測。相對於傳 統的內部模型法(internal model approach),此新方法將可適用於更廣泛 類型的干擾。以上提出的三個方法可同時估測出參數及干擾。 以上兩種線上估測的設計方法經過一些調整後可運用在時變參數識 別的問題上。其細節將於本文中作描述。 關鍵字: 強韌參數識別,時變參數識別,干擾識別,卡曼濾波器。 Two subjects of continuous-time parameter identification problems expressed in linear regression form are discussed in this thesis. One is the time-invariant parameter identification while subject to non-stochastic disturbances termed as the robust identification. The other is the time-variant parameter identification. In addition to the measurement stochastic noise, the output signal of a system is usually contaminated with the non-stochastic disturbances which are usually resulted from errors of measure devices, system unmodled dynamics or the process disturbances acting on the system. Most identifications considering the disturbance as a white noise will have biased estimates while subject to these kinds of disturbances. In the parameterization, one can lump all the disturbances into one disturbance term at the output expressed in linear regression form. We proposes one off-line approach and two on-line approaches to deal with this problem. In the off-line approach, the unknown disturbance will be approximately expanded by a finite Fourier cosine series with unknown coefficients. The unknown coefficients and the known basis functions will be augmented to the original parameter vector and the regressor respectively. With the expanded regressor, one can obtain the estimates of the expanded parameter vector by adopting the least-squares batch calculation. A necessary condition on persistent excitation of the expanded regressor is proposed too. In the first of the two on-line approaches, the estimation scheme is built under the structure of gradient algorithm. A compensation is made to reject the effect of the disturbance in the estimation error dynamics by designing a stabilized controller. In the design procedure, the averaging method is used for system approximation and the $H_{infty}$ frequency shaping methodology is utilized to synthesize the controller. The control signal will be able to track the disturbance signal and cancel it in the estimation error dynamics and that guarantees the convergence of the parameter estimation. In the second of the on-line approaches, an state-observer based estimator is constructed. To include the estimation of the disturbance into the estimation scheme, the system plant is augmented with the model of the proposed disturbance generating filter also termed as dynamics extension filter. The Kalman filter is adopted to perform the states estimation. Compared with the conventional internal model approach, the proposed method could be applied to a more general disturbance class. The three proposed approaches can identify both parameters and the disturbance simultaneously. The design procedures of the above two on-line approaches can be grafted to the time-variant parameter identification problem with some modifications. Special consideration will be addressed in the context. Keywords: Robust identification, Time-variant parameter identification, Disturbance identification, Kalman filter. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/63684 |
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顯示於系所單位: | 機械工程學系 |
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