本文關鍵詞： 多變量線性系統模型 迭代或遞推參數辨識 閉環(huán) 抗擾設計　出處：《北京化工大學》2016年博士論文　論文類型：學位論文

【摘要】：多數情況下,出于安全和經濟方面的考慮,在閉環(huán)條件下開展系統辨識的研究十分有必要。另一方面,在不同復雜外部干擾影響下,如何構建出在操作點附近的合理線性化模型以及如何對其進行參數估計,是系統辨識領域具有廣泛意義的、極其重要的研究課題�；谏鲜銮樾�,假定系統模型結構確定或基本確定,本文主要研究針對幾種典型多變量線性系統模型的參數抗擾估計新方法,并通過對噪聲進行分析、對輸入信號以及算法進行精心設計,使得這些改進后的估計方法具有很強的抗擾特性。在開環(huán)閉環(huán)條件下,本文重點討論以下幾個問題：輸入信號的設計、閉環(huán)框架的合適選擇、不同類型噪聲對算法的影響和改造、結構簡化帶來的辨識誤差的有效消除以及復雜算法的收斂性分析。由于不同的線性系統模型之間具有內在聯系且在某些條件下能夠相互轉化,針對不同線性系統開發(fā)出的辨識方法既具有特殊性又具有較強的泛化能力。針對不同的多變量線性系統模型,本文的主要工作和創(chuàng)新點表現在如下幾個方面：1、針對閉環(huán)多變量積分和不穩(wěn)定過程,提出一種全新的迭代最小二乘辨識方法,這種針對滯后的迭代計算能夠有效減小對滯后取一階泰勒近似所產生的誤差,因而在噪聲環(huán)境中擁有相當快的收斂效率。通過等效的輸入和輸出,這種新型算法能夠拓展到多變量積分和不穩(wěn)定過程的閉環(huán)辨識中,且對生產實踐有一定的指導意義。2、在工程實踐中,由于測量數據包含離群點,其分布是非高斯的。這種情況會導致參數估計器的表現性能顯著下降。針對重尾t分布噪聲影響下的離散多輸入多輸出系統,本文創(chuàng)造性地提出一種迭代再賦權重的相關分析方法。通過將多變量相關分析和以t分布為基礎的魯棒M估計器相結合,該迭代方法能夠獲得在重尾t噪聲下的魯棒有限脈沖響應(FIR)模型。3、本文針對閉環(huán)條件下的離散多變量方程誤差類模型開發(fā)出一種提升的迭代辨識方法并應用于閉環(huán)直接辨識中。該算法基于分層辨識原則對有色噪聲進行有效處理,因而具備很強的抗干擾能力。在閉環(huán)辨識中,輸入測試信號的設計確保了閉環(huán)系統的可識別性,彈性化以及獨立參數化的噪聲模型使得閉環(huán)辨識偏差最小化。4、針對帶有稀少測量的離散多變量輸出誤差類模型,本文提出一種新型的以輔助模型為基礎的多新息最小二乘算法,該算法將標量新息拓展到新息向量并且利用輔助模型的輸出來取代信息矩陣中的內部未知變量。為了很好地處理稀少測量模式,算法采用變間隔遞推的形式進而跳過不可用數據(包括離群點)。最后,利用鞅收斂定理,本文證實了該辨識算法的收斂性。
[Abstract]:In most cases, for security and economic reasons, it is necessary to study system identification under closed loop conditions. On the other hand, under the influence of different complex external disturbances, How to construct a reasonable linearization model near the operating point and how to estimate its parameters are of great significance in the field of system identification. Assuming that the system model structure is determined or basically determined, this paper mainly studies a new method of parameter immunity estimation for several typical multivariable linear system models. By analyzing the noise, the input signal and the algorithm are carefully designed. Under the condition of open loop and closed loop, the following problems are discussed in this paper: the design of input signal, the suitable selection of closed loop frame, The influence and modification of different types of noise on the algorithm, The efficient elimination of identification error caused by structural simplification and the convergence analysis of complex algorithms. Due to the inherent relationship between different linear system models and the ability to transform each other under certain conditions, The identification methods developed for different linear systems have both particularity and strong generalization ability. The main work and innovation of this paper are as follows: 1. For closed-loop multivariable integral and unstable process, a new iterative least square identification method is proposed. This iterative computation for delay can effectively reduce the error caused by taking the first order Taylor approximation to delay, and thus has a fairly fast convergence efficiency in noisy environment. This new algorithm can be extended to the closed-loop identification of multivariable integrals and unstable processes, and has a certain guiding significance for production practice. In engineering practice, because the measured data contain outliers, Its distribution is not Gao Si's. This kind of situation will cause the performance of the parameter estimator to degrade significantly. For the discrete multiple-input multi-output system under the influence of the heavy-tailed t distribution noise, In this paper, we creatively propose an iterative re-weighted correlation analysis method, which combines multivariate correlation analysis with robust M-estimator based on t distribution. This iterative method can obtain robust finite impulse response model. 3. This paper develops an improved iterative identification method for the error class model of discrete multivariable equations under closed loop condition and applies it to closed loop. In the direct ring identification, the algorithm is based on the hierarchical identification principle to deal with colored noise effectively. In the closed-loop identification, the design of the input test signal ensures the identifiability of the closed-loop system. The elastic and independent parameterized noise model minimizes the closed-loop identification bias. For the discrete multivariable output error class model with sparse measurements, a new multi-innovation least squares algorithm based on the auxiliary model is proposed in this paper. The algorithm extends the scalar innovation to the innovation vector and uses the output of the auxiliary model to replace the internal unknown variables in the information matrix. The algorithm uses the form of variable interval recursion to skip the unusable data (including outliers). Finally, by using the martingale convergence theorem, the convergence of the identification algorithm is proved in this paper.
【學位授予單位】：北京化工大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：N945.14

【參考文獻】

相關期刊論文 前4條

1 黃金峰;張合新;李旭淵;;逆重復M序列相關辨識法的一種改進[J];彈箭與制導學報;2011年03期

2 丁鋒;劉小平;;多變量輸出誤差系統的輔助模型隨機梯度辨識算法(英文)[J];自動化學報;2010年07期

3 吳慶憲,丁勇,胡壽松;多維逆M序列及其在多變量系統辨識中的應用[J];數據采集與處理;2000年02期

4 王秀峰;盧桂章;;多變量線性系統的遞推辨識算法[J];自動化學報;1981年04期

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本文編號：1507988