An Extended Closed-loop Subspace Identification Method for Error-in-variables Systems

A closed-loop subspace identification method is proposed for industrial systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem. Using the orthogonal projection approach to eliminate the noise influence, consistent estimation is guaranteed for the deterministic part of such a system. A strict proof is given for analyzing the rank condition for such orthogonal projection, in order to use the principal component analysis (PCA) based singular value decomposition (SVD) to derive the extended observability matrix and lower triangular Toeliptz matrix of the plant state-space model. In the result, the plant state matrices can be retrieved in a transparent manner from the above matrices. An illustrative example is shown to demonstrate the effectiveness and merits of the proposed subspace identification method.

Subspace Identification for Closed-Loop Systems With Unknown Deterministic Disturbances

This paper presents a subspace identification method for closed-loop systems with unknown deterministic disturbances.To deal with the unknown deterministic disturbances,two strategies are implemented to construct the row space that can be used to approximately represent the unknown deterministic disturbances using the trigonometric functions or Bernstein polynomials depending on whether the disturbance frequencies are known.For closed-loop identification,CCF-N4SID is extended to the case with unknown deterministic disturbances using the oblique projection.In addition,a proper Bernstein polynomial order can be determined using the Akaike information criterion(AIC)or the Bayesian information criterion(BIC).Numerical simulation results demonstrate the effectiveness of the proposed identification method for both periodic and aperiodic deterministic disturbances.

A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm

Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.

Closed-Loop System Identification Approach of the Inertial Models

The mathematical model that approximates the dynamics of the industrial process is essential for the efficient synthesis of control algorithms in industrial applications. The model of the process can be obtained according to the identification procedures in the open-loop, or in the closed-loop. In the open-loop, the identification methods are well known and offer good process approximation, which is not valid for the closed-loop identification, when the system provides the feedback output and doesn’t permit it to be identified in the open-loop. This paper offers an approach for experimental identification in the closed-loop, which supposes the approximation of the process with inertial models, with or without time delay and astatism. The coefficients are calculated based on the values of the critical transfer coefficient and period of the underdamped response of the closed-loop system with P controller, when system achieves the limit of stability. Finally, the closed-loop identification was verified by the computer simulation and the obtained results demonstrated, that the identification procedure in the closed-loop offers good results in process of estimation of the model of the process.

Study of Stochastic Subspace System Identification Method

Stochastic Subspace Identification (SSI) is a novel time domain identification method, which directly uses operational response data to identify the system model by linear algebraic manipulations such as QR factorization and Singular Value Decomposition (SVD). This paper deals with SSI and its applications for structural modal identification. The NASA mini mast model is used for simulations to illustrate how to select input parameters, and to demonstrate identification precision. A real building structure, the Heritage Court Tower (HCT) in Canada is analyzed. From the simulation and test researches, the conclusions can be made to instruct how to identify structural modal parameters using SSI method.

PEMFC Fractional-order Subspace Identification Model

A proton exchange membrane fuel cell(PEMFC)is a new type of hydrogen fuel cell that plays an indispensable role in an energy network.However,the multivariable and fractional-order characteristics of PEMFC make it difficult to establish a practical model.Herein,a fractional-order subspace identification model based on the adaptive monarch butterfly optimization algorithm with opposition-based learning(ALMBO)algorithm is proposed for PEMFC.Introducing the fractional-order theory into the subspace identification method by adopting a Poisson filter for with input and output data,a weight matrix is proposed to improve the identification accuracy.Additionally,the ALMBO algorithm is employed to optimize the parameters of the Poisson filter and fractional order,which introduces an opposition-based learning strategy into the migration operator and incorporates adaptive weights to improve the optimization accuracy and prevent falling into a locally optimal solution.Finally,the PEMFC fractional-order subspace identification model is established,which can accurately describe the dynamic process of PEMFC.

SUBSPACE METHOD FOR BLIND IDENTIFICATION OF CDMA TIME-VARYING CHANNELS

A new blind method is proposed for identification of CDMA Time-Varying (TV)channels in this paper. By representing the TV channel's impulse responses in the delay-Doppler spread domain, the discrete-time canonical model of CDMA-TV systems is developed and a subspace method to identify blindly the Time-Invariant (TI) coordinates is proposed. Unlike existing basis expansion methods, this new algorithm does not require .estimation of the base frequencies, neither need the assumption of linearly varying delays across symbols. The algorithm offers definite explanation of the expansion coordinates. Simulation demonstrates the effectiveness of the algorithm.

IMPROVED COVARIANCE DRIVEN BLIND SUBSPACE IDENTIFICATION METHOD

An improved covariance driven subspace identification method is presented to identify the weakly excited modes. In this method, the traditional Hankel matrix is replaced by a reformed one to enhance the identifiability of weak characteristics. The robustness of eigenparameter estimation to noise contamination is reinforced by the improved Hankel matrix, in combination with component energy index （CEI） which indicates the vibration intensity of signal components, an alternative stabilization diagram is adopted to effectively separate spurious and physical modes. Simulation of a vibration system of multiple-degree-of-freedom and experiment of a frame structure subject to wind excitation are presented to demonstrate the improvement of the proposed blind method. The performance of this blind method is assessed in terms of its capability in extracting the weak modes as well as the accuracy of estimated parameters. The results have shown that the proposed blind method gives a better estimation of the weak modes from response signals of small signal to noise ratio （SNR）and gives a reliable separation of spurious and physical estimates.

MODELING INTRAPERSONAL DEFORMATION SUBSPACE USING GMM FOR PALMPRINT IDENTIFICATION

In this paper, an efficient model of palmprint identification is presented based on subspace density estimation using Gaussian Mixture Model (GMM). While a few training samples are available for each person, we use intrapersonal palmprint deformations to train the global GMM instead of modeling GMMs for every class. To reduce the dimension of such variations while preserving density function of sample space, Principle Component Analysis (PCA) is used to find the principle differences and form the Intrapersonal Deformation Subspace (IDS). After training GMM using Expectation Maximization (EM) algorithm in IDS, a maximum likelihood strategy is carried out to identify a person. Experimental results demonstrate the advantage of our method compared with traditional PCA method and single Gaussian strategy.

Nonlinear System Identification Using Methods for Subspace

The identification of Wiener systems has been an active research topic for years. A Wiener system is a series connection of a linear dynamic system followed by a static nonlinearity. The difficulty in obtaining a representation of the Wiener model is the need to estimate the nonlinear function from the input and output data, without the intermediate signal availability. This paper presents a methodology for the nonlinear system identification of a Wiener type model, using methods for subspaces and polynomials of Chebyshev. The subspace methods used are MOESP （multivariable output-error state space） and N4SID （numerical algorithms for subspace state space system identification）. A simulated example is presented to compare the performance of these algorithms.

Identification of Continuous-Time Linear Aircraft Models Using Subspace Identification

This paper considers the possibility of estimating continuous-time linear aircraft models by using the subspace identification The outline of the subspace identification for estimating the continuous-time linear model is first mentioned. The identification simulation in which the subspace identification is then applied to the modeling of aircraft in the lateral motion is performed. As a result, the continuous-time linear aircraft model is appropriately estimated by the subspace identification if the amount of the measurement noise is not so much.

Model Identification Based on Subspace Model Identification of Main Steam Temperature in Ultra-Supercritical Coal-Fired Power Unit

Ultra-supercritical(USC) unit is more and more popular in coal-fired power industry.In this paper,closed-loop identification based on subspace model identification(SMI) is introduced for superheated steam temperature system of USC unit.Closed-loop SMI is applied to building step response model of the unit directly.The parameters selection method is proposed to deal with the parameter sensitivity and improve the reliability of the model.Finally,the model is used in model identification of real USC unit.

PEMFC Identification Based on a Fractional-Order Hammerstein State-Space Model with ADE-BH Optimization

Considering the fractional-order and nonlinear characteristics of proton exchange membrane fuel cells(PEMFC),a fractional-order subspace identification method based on the ADE-BH optimization algorithm is proposed to establish a fractional-order Hammerstein state-space model of PEMFCs.Herein,a Hammerstein model is constructed by connecting a linear module and a nonlinear module in series to precisely depict the nonlinear property of the PEMFC.During the modeling process,fractional-order theory is combined with subspace identification,and a Poisson filter is adopted to enable multi-order derivability of the data.A variable memory method is introduced to reduce computation time without losing precision.Additionally,to improve the optimization accuracy and avoid obtaining locally optimum solutions,a novel ADEBH algorithm is employed to optimize the unknown parameters in the identification method.In this algorithm,the Euclidean distance serves as the theoretical basis for updating the target vector in the absorption-generation operation of the black hole(BH)algorithm.Finally,simulations demonstrate that the proposed model has small output error and high accuracy,indicating that the model can accurately describe the electrical characteristics of the PEMFC process.

Full-parameter constrained parsimonious subspace identification with steady-state information for DC–DC converters

A full-parameter constrained parsimonious subspace identification method that incorporates the steady-state a priori infor-mation of the system is proposed to model the DC-DC converters.A parsimonious model with fewer parameters is used to represent the system,and then an optimal weighted methods is used to estimate the system parameters matrices by taking into account both dynamical data and steady-state data.Compared with traditional data-driven methods for DC-DC convert-ers,the subspace-based method can simultaneously estimate model structure and parameter with appropriate computational complexity.Moreover,compared with the traditional full-parameter constrained subspace approach,the proposed algorithm can accurately estimate the system parameters with a smaller variance.The experimental results on a DC-DC synchronous buck converter verify the effectiveness and superiority of the proposed method.

Closed-loop identification of systems using hybrid Box–Jenkins structure and its application to PID tuning

The paper describes a closed-loop system identification procedure for hybrid continuous-time Box–Jenkins models and demonstrates how it can be used for IMC based PID controller tuning. An instrumental variable algorithm is used to identify hybrid continuous-time transfer function models of the Box–Jenkins form from discretetime prefiltered data, where the process model is a continuous-time transfer function, while the noise is represented as a discrete-time ARMA process. A novel penalized maximum-likelihood approach is used for estimating the discrete-time ARMA process and a circulatory noise elimination identification method is employed to estimate process model. The input–output data of a process are affected by additive circulatory noise in a closedloop. The noise-free input–output data of the process are obtained using the proposed method by removing these circulatory noise components. The process model can be achieved by using instrumental variable estimation method with prefiltered noise-free input–output data. The performance of the proposed hybrid parameter estimation scheme is evaluated by the Monte Carlo simulation analysis. Simulation results illustrate the efficacy of the proposed procedure. The methodology has been successfully applied in tuning of IMC based flow controller and a practical application demonstrates the applicability of the algorithm.

Data-Driven Model Identification and Control of the Inertial Systems

In the synthesis of the control algorithm for complex systems, we are often faced with imprecise or unknown mathematical models of the dynamical systems, or even with problems in finding a mathematical model of the system in the open loop. To tackle these difficulties, an approach of data-driven model identification and control algorithm design based on the maximum stability degree criterion is proposed in this paper. The data-driven model identification procedure supposes the finding of the mathematical model of the system based on the undamped transient response of the closed-loop system. The system is approximated with the inertial model, where the coefficients are calculated based on the values of the critical transfer coefficient, oscillation amplitude and period of the underdamped response of the closed-loop system. The data driven control design supposes that the tuning parameters of the controller are calculated based on the parameters obtained from the previous step of system identification and there are presented the expressions for the calculation of the tuning parameters. The obtained results of data-driven model identification and algorithm for synthesis the controller were verified by computer simulation.

Automatic modal parameter identification of high arch dams:feasibility verification

Modal parameters, including fundamental frequencies, damping ratios, and mode shapes, could be used to evaluate the health condition of structures. Automatic modal parameter identification, which plays an essential role in realtime structural health monitoring, has become a popular topic in recent years. In this study, an automatic modal parameter identification procedure for high arch dams is proposed. The proposed procedure is implemented by combining the densitybased spatial clustering of applications with noise(DBSCAN) algorithm and the stochastic subspace identification(SSI). The 210-m-high Dagangshan Dam is investigated as an example to verify the feasibility of the procedure. The results show that the DBSCAN algorithm is robust enough to interpret the stabilization diagram from SSI and may avoid outline modes. This leads to the proposed procedure obtaining a better performance than the partitioned clustering and hierarchical clustering algorithms. In addition, the errors of the identified frequencies of the arch dam are within 4%, and the identified mode shapes are in agreement with those obtained from the finite element model, which implies that the proposed procedure is accurate enough to use in modal parameter identification. The procedure is feasible for online modal parameter identification and modal tracking of arch dams.

Application of Array Measurement in Blind Identification

This paper deals with blind identification and deconvolution algorithm for an arbitrary, possibly white or colored, stationary or nonstationary signal, which is observed through array sensors. By using multiple sensors with their individual outputs sampled at a rate 1/T, one can obtain cyclostationary signals. They can be considered as a single-input multiple-output model with an identical but unknown input signal. With the array measurement, an algorithm for estimating the system transfer function model and its parameters is presented.

基于滑窗OPTICS算法和DATA-SSI算法的桥梁模态参数智能化识别

针对现有基于数据驱动的随机子空间(data-driven stochastic subspace identification,DATA-SSI)算法存在的不足,无法实现稳定图中真假模态的智能化筛选,提出了一种新的模态参数智能化识别算法。首先通过引入滑窗技术来实现对输入信号的合理划分,以避免虚假模态和模态遗漏现象的出现;其次通过引入OPTICS(ordering points to identify the clustering structure)密度聚类算法实现稳定图中真实模态的智能化筛选,最后将所提算法运用于某实际大型斜拉桥主梁结构的频率和模态振型识别过程中。结果表明,所提改进算法识别的频率值结果与理论值(MIDAS有限元结果)以及实际值(现场动力特性实测结果)间的误差均在5%以内,且识别的模态振型图与理论模态振型图具有很高的相似性。