【摘要】：針對非線性結(jié)構(gòu)系統(tǒng)時(shí)變參數(shù)識別問題,傳統(tǒng)無跡卡爾曼濾波(Unscented Kalman Filter,UKF)難以有效跟蹤結(jié)構(gòu)參數(shù)的變化。將強(qiáng)跟蹤濾波原理引入無跡卡爾曼濾波,提出一種強(qiáng)跟蹤無跡卡爾曼濾波(Strong Tracking Unscented Kalman Filter,STUKF)算法,以識別結(jié)構(gòu)參數(shù)的變化。在UKF量測更新后,依據(jù)輸出殘差計(jì)算漸消因子矩陣;引入兩個漸消因子矩陣實(shí)時(shí)調(diào)整狀態(tài)預(yù)測協(xié)方差矩陣,使殘差序列強(qiáng)行正交,快速修正結(jié)構(gòu)參數(shù)估計(jì)值,使STUKF具有對結(jié)構(gòu)參數(shù)變化的跟蹤能力;此外,為節(jié)省計(jì)算時(shí)間,調(diào)整狀態(tài)預(yù)測協(xié)方差矩陣后不再進(jìn)行sigma點(diǎn)采樣,保證了算法的高效性。數(shù)值分析結(jié)果表明,該算法能有效識別非線性結(jié)構(gòu)系統(tǒng)的參數(shù)及其變化,并具有較強(qiáng)的抗噪性。
[Abstract]:The traditional unscented Kalman filter (unscented Kalman filter UKF) is difficult to track the variation of structural parameters effectively for the identification of time-varying parameters of nonlinear structural systems. A strong tracking unscented Kalman filter (STUKF) algorithm is proposed to identify structural parameters by introducing the principle of strong tracking filtering into unscented Kalman filter. After the UKF measurement is updated, the fading factor matrix is calculated according to the output residuals, and two fading factor matrices are introduced to adjust the state prediction covariance matrix in real time. In addition, in order to save calculation time, the state prediction covariance matrix can not be sampled by sigma, which ensures the high efficiency of the algorithm. Numerical results show that the proposed algorithm can effectively identify the parameters and their variations of nonlinear structural systems, and has strong noise resistance.
【作者單位】： 蘭州理工大學(xué)防震減災(zāi)研究所;蘭州理工大學(xué)西部土木工程防災(zāi)減災(zāi)教育部工程研究中心;
【基金】：國家自然科學(xué)基金(51578274;51568041) 教育部長江學(xué)者創(chuàng)新團(tuán)隊(duì)項(xiàng)目(IRT13068) 甘肅省青年科技基金計(jì)劃(2014GS03277)
【分類號】：TN713
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本文編號：2128571