Dynamic Stability Analysis of Linear Time-varying Systems via an Extended Modal Identification Approach

The problem of linear time-varying（LTV） system modal analysis is considered based on time-dependent state space representations, as classical modal analysis of linear time-invariant systems and current LTV system modal analysis under the ＂frozen-time＂ assumption are not able to determine the dynamic stability of LTV systems. Time-dependent state space representations of LTV systems are first introduced, and the corresponding modal analysis theories are subsequently presented via a stabilitypreserving state transformation. The time-varying modes of LTV systems are extended in terms of uniqueness, and are further interpreted to determine the system＇s stability. An extended modal identification is proposed to estimate the time-varying modes, consisting of the estimation of the state transition matrix via a subspace-based method and the extraction of the time-varying modes by the QR decomposition. The proposed approach is numerically validated by three numerical cases, and is experimentally validated by a coupled moving-mass simply supported beam exper- imental case. The proposed approach is capable of accurately estimating the time-varying modes, and provides anew way to determine the dynamic stability of LTV systems by using the estimated time-varying modes.

TIME DOMAIN PARAMETERS IDENTIFICATION OF FOUNDATION-STRUCTURE INTERACTION SYSTEM

The time domain parameter laenuncauon memoa oi me iounuauon-structure interaction system is presented. On the basis of building the computation mode and the motion equation of the foundation-structure interaction system, the system parameter identification method was established by using the extended Kalman filter （EKF） technique and taking the unknown parameters in the system as the augment state variables. And the time parameter identification process of the foundation-structure interaction system was implemented by using the data of the layer foundation-storehouse interaction system model test on the large vibration platform. The computation result shows that the established parameter identification method can induce good parameter estimation.

Time of Liver Function Abnormal Identification on Prediction of the Risk of Anti-tuberculosis-induced Liver Injury

Background and Aims:Anti-tuberculosis(anti-TB)druginduced liver injury(AT-DILI)is the most common side effect in patients who received anti-TB therapy.AT-DILI management includes monitoring liver function until symptoms arise in patients without high-risk factors for liver damage.The present study aimed to investigate the effect of liver function test(LFT)abnormal identification on the risk of DILI,including liver failure and anti-TB drug resistance in patients without high-risk factors.Methods:A total of 399 patients without high-risk factors for liver damage at baseline and who experienced LFT abnormal during the 6 months of first-line anti-TB treatment were enrolled.The Roussel Uclaf Causal Relationship Assessment Method(RUCAM,2016)was applied in suspected DILI.The correlations between the time of LFT abnormal identification and DILI,liver failure,and anti-TB drug resistance were analyzed by smooth curve fitting and multivariable logistic regression models.Results:Among all study patients,131 met the criteria for DILI with a mean RUCAM causality score of 8.86±0.63.26/131 and 105/131 were in the probable grading and highly probable grading,respectively.The time of abnormal LFT identification was an independent predictor of DILI,liver failure,and anti-TB drug resistance in the crude model and after adjusting for other risk patient factors.The time of abnormal LFT identification was positively correlated with DILI,liver failure,and anti-TB drug resistance.The late identification group(>8 weeks)had the highest risk of DILI,followed by liver failure compared with the other two groups.Conclusions:The time to identification of LFT was positively correlated with DILI,liver failure,and anti-TB drug resistance.The risk of DILI and liver failure was significantly increased in the late identification group with abnormal LFT identified after 8 weeks compared with 4 and 8 weeks.Early monitoring of LFT is recommended for patients without the high-risk factor of DILI after anti-TB treatment is initiated.

KEY TECHNOLOGY IN INTELLIGENT CONTROL FOR SHEET METAL DEEP DRAWING

Intellectualization of sheet metal in deep drawing is a new combined technology, which is concerned with control science and computer science and sheet metal forming theory. The intelligent control system for sheet metal deep drawing consists of four fundamental factors: real time measurement, identification, prediction and control. Real time identification of material properties and friction coefficient is the most important factor in the whole system. An artificial neural network model for identification of the material properties and friction coefficient was established according to deep drawing characteristics and more automation. The identification of the material properties and friction coefficient was realized.

An integrated approach to modeling and adaptive control

In the book(Adaptive Identification,Prediction and Control-Multi Level Recursive Approach),the concept of dynamical linearization of nonlinear systems has been presented.This dynamical linearization is formal only,not a real linearization.From the linearization procedure,we can find a new approach of system identification,which is on-line real-time modeling and real-time feedback control correction.The modeling and real-time feedback control have been integrated in the identification approach,with the parameter adaptation model being abandoned.The structure adaptation of control systems has been achieved,which avoids the complex modeling steps.The objective of this paper is to introduce the approach of integrated modeling and control.