Model Identification Method Research Articles

Identification of LPV Models with Two Scheduling Variables Using Transition Test

This paper presents the research findings of identification method for LPV models with two scheduling variables using transition test. The LPV model is parameterized as blended linear models, which is also called as multi-model structure. Linear weighting functions are used as the local model weights and the Gauss-Newton method is used to optimize the nonlinear LPV model parameters. Usefulness of the method is verified by modeling a high purity distillation column, the case study shows that the multi-model LPV models can yield a better model accuracy with respect to simulation outputs. The identification method proposed in this paper can be used in batch process identification.

Time Series Decomposition: An Additional Methodology

Our aim in this article is to introduce an additional approach that extends the conventional model identification method to decompose time series into the stationary and random walk components. It has been shown that the additional approach of decomposition has reliable small sample properties, especially for high-frequency data.

High acceleration motion control based on a time-domain identification method and the disturbance observer

High acceleration motion tends to excite flexible modes of the motion stage to magnify disturbances, causing challenges for control engineers. Among existing control algorithms, the disturbance observer (DOB) is recognized as an effective tool for improving servo performance by suppressing disturbances. Besides external disturbances, the internal interference caused by model mismatches is an important factor which can degrade the servo performances of the DOB-based controllers. Therefore, a more accurate relay-based model identification method is proposed to deal with model mismatches. This method uses the position signal as the input of the relay module. Simulations show that the proposed identification method achieves higher identification accuracy by preventing the amplification of the quantization error during velocity detection. High acceleration point-to-point motions are conducted on a linear-driving stage with a DOB-based controller and experimental results demonstrate that improved servo performance with a maximum tracking error of 2.0μm at a maximum acceleration of 9.1g (1g=9.806m/s2).

Modeling and Control for a Small UAV Helicopter

A frequency identification modeling method for a small UAV helicopter and its control system design is presented. A modified frequency identification method for a state-space model of helicopter is presented. The overall concept is to extract a complete set of non-parametric input-to-output frequency responses that fully characterizes the coupled helicopter dynamics, conduct a nonlinear search based on secant method for a linear state-space model that matches the frequency-response data set. The accuracy of the identified model is verified by flight experiments. A path following controller is presented for an unmanned helicopter with two-loop control frame. The outer-loop is used to obtain the expected attitude angles through reference path and speed with guidance-based path following control, and the inner-loop is used to control the attitude angles of helicopter tracking the expected ones with loop shaping method. Finally, an 8 trajectory tracking simulation is conducted to illustrate the efficiency of the proposed control method.

Parsimonious rainfall-runoff model construction supported by time series processing and validation of hydrological extremes – Part 2: Intercomparison of models and calibration approaches

An intercomparison of different approaches for the construction and calibration of lumped conceptual rainfall-runoff models is made based on two case studies with unrelated meteorological and hydrological characteristics located in two regions, Belgium and Kenya. While a model with pre-fixed “one-size-fits-all” model structure is traditionally used in lumped conceptual rainfall-runoff modeling, this paper shows the advantages of model structure inference from data or field evidence in a case-specific and step-wise way using non-commensurable measures derived from observed series. The step-wise model structure identification method does not lead to higher accuracy than the traditional approach when evaluated using common statistical criteria like the Nash–Sutcliffe efficiency. The method is, however, favorable to produce a well-balanced calibration obtaining accurate results for a wide range of runoff properties: total flows, quick and slow subflows, cumulative volumes, peak flows, low flows, frequency distributions of peak and low flows, changes in quick flows for given changes in rainfall. It furthermore is shown that model performance evaluation procedures that account for the flow residual serial dependency and homoscedasticity are preferred. Explicit evaluation of model results for peak and/or low flow extremes and changes in these extremes make the models useful for impact investigations on such hydrological extremes.

Identification of Complex Models

A general noninvasive method for identification of uncertain parametric models is proposed. It can be applied to a broad range of calculations and simulations, e.g., for finite element models of electromagnetism, fluids and mechanical structures, dynamic modeling and signal processing, or even econometric models. The model statistics are represented by a small and hence efficient deterministic ensemble containing samples of parameter sets, which is the object of identification. In contrast to random ensembles, deterministic ensembles are calculated with definite rules and hence are well-defined and completely reproducible, i.e., possible to calibrate. Upon identification, the prior ensemble is calibrated to a posterior ensemble with Bayesian inference. For model inversion, a surrogate model is found by linear regression. To maximize its quality, the regression points of the surrogate model can be optimized to match the sampling points of the deterministic ensemble by means of fixed-point iterations. Ident...

Segmentation Methods for Model Identification from Historical Process Data

In industry, system identification is a time-consuming exercise that impacts the profitability and safety of the plant. One way to avoid this problem is to use stored historical process data in estimating the required process models. Given the large amount of data available, which is often corrupted due to process disruptions, loss of information, and poor data quality, automated segmentation of the data set would be an invaluable asset. Recently, two different methods have been proposed to accomplish this task: one based on Laguerre models and one based on autoregressive with exogenous input (ARX) models. In this paper, the Laguerre approach will be analysed and it will be shown that the results are dependent on selecting appropriate Laguerre model parameters and input signals, while relatively insensitive to the variance thresholds. Furthermore, this approach has a tendency to overpartition the data set based on the smallest changes in the process. Therefore, in order to decrease the number of models identified it is proposed to couple this method with an entropy-based metric for determining similar models. Based on simulations that include this entropy metric, it is shown that a reduction in model partitions is obtained.

A Subspace-based Wiener System Identification Method for the Individualized Anesthesia Care

Abstract This study focuses on the individual sedation model identification problem, and proposes a subspace-based Wiener system identification method. The traditional compartmental pharmacokinetics-pharmacodynamics hypnosis model is considered as a specific Wiener system with the Hill equation nonlinear term. To deal with the Hill nonlinear term, the proposed method employs a set of specific bases to turn the Wiener system into a linear one, and then the subspace orthogonal projection identification method has been implemented to identify the transformed linear model. Compared with the traditional anesthesia model identification methods, the proposed method can effectively overcome the shortage of measurement data, get rid of the estimation of the effect compartment concentration which is impossible to be measured, and improve the individualization performance of the identified model. A simulation study on 24 various virtual patients from the Wang's Simulator has been conducted and validates the efficiency and robustness of the proposed method, and a drug infusion instruction has been provided in order to get relatively accurate identification performance.

A novel recursive subspace identification approach of closed-loop systems

In this paper, a subspace model identification method under closed-loop experimental condition is presented which can be implemented to recursively identify and update the system model. The projected matrices play an important role in this identification scheme which can be obtained by the projection of the input and output data onto the space of exogenous inputs and recursively updated through sliding window technique. The propagator type method in array signal processing is then applied to calculate the subspace spanned by the column vectors of the extended observability matrix without singular value decomposition. The speed of convergence of the proposed method is mainly dependent on the number of block Hankel matrix rows and the initialization accuracy of the projected data matrices. The proposed method is feasible for the closed-loop system contaminated with coloured noises. Two numerical examples show the effectiveness of the proposed algorithm.

A GRAY-BOX NEURAL NETWORK-BASED MODEL IDENTIFICATION AND FAULT ESTIMATION SCHEME FOR NONLINEAR DYNAMIC SYSTEMS

A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

Projection-based identification algorithm for grey-box continuous-time models

In this paper, a new identification method for continuous-time models, which can handle various grey-box structures and has strong robustness, is presented. The proposed method is based on an incremental model update scheme and the projection onto the subspace which reflects the model structure. By utilising these schemes, robustness of other continuous-time system identification methods and versatility of generic optimisation algorithms can be integrated into the proposed method. The effectiveness of the proposed method is demonstrated through numerical examples related to a grey-box model in closed-loop system and systems with unknown time-delay.

The Comparison of Parameter Identification Methods for Fractional, Partial Differential Equation

In this paper the parameter identification methods for nonlinear models were compared for fractional, partial differential equation. The compared three methods are: the Levenberg-Marquardt algorithm, the Gauss-Newton algorithm and Nelder-Mead Simplex method. The series of numerical experiments were performed to test their robustness and calculation speed. The result of this tests were presented and described.

Characteristic model of travelling wave ultrasonic motor

In general, the design and analysis of ultrasonic motor and motor’s control strategy are based on mathematical model. The academic model is widely used in the analysis of traveling wave ultrasonic motor (TWUSM). But the dispersive characteristic of piezoelectric ceramics and other complicated process, such as the friction, make the model’s precision not so accurate. On the other hand, identification modeling method, which is built based on the tested data, has obtained increasing application in the study of ultrasonic motor’s control technology. Based on the identification model, many control strategies can be designed easily. But the identification model is an approximate model, so if a more accurate model of ultrasonic motor can be obtained, the analysis and design of motor control system will be more effective. Characteristic model is a kind of identification model which can accurately describe the characteristics of TWUSM. Based on the tested data, this paper proposes the modeling method of ultrasonic motor’s characteristic model. The paper also makes a comparison of the effectiveness of different identification algorithms. Aiming at the speed control of ultrasonic motor, the influence of the parameter’s initial values on the precision of model is discussed. The calculating results indicate the availability of this characteristic model.

Research on Wind Turbine Generator Dynamic Reliability Test System Based on Feature Recognition

Wind power resource development is increasingly becoming the focus of the current research and development in various countries' relevant scientific institutions. To make sure the secure and reliable operation of wind turbine generator, the study develops the wind turbine generator dynamic reliability test system. When the fault of gearbox and spindle occurs, their features of vibration signals are special. According to the feature recognition technology, the application of time and frequency domain model identification method has practical significance to the test system. Based on Bayesian network fault diagnosis method, the vibration feature recognition system of wind turbine generator is constructed. Finally, the paper uses GPRS technology to realize the wireless transmission of operation information. The wind turbine generator dynamic reliability test system is built based on GPRS technology to realize automatic control and remote intelligent monitoring and to ensure the safe and stable operation of wind farms.

Approach of Synthesizing Model Predictive Control and Its Applications for Rotary Kiln Calcination Process

An actual control demand of rotary kiln is taken as background. By analyzing and improving approach of S-MPC (synthesizing model predictive control), an effective strategy which applies complex S-MPC in actual industrial process is designed. Firstly, after analyzing the main components technology and calcination reaction mechanism in detail, the calcining belt state-space model of rotary kiln is built using PO-Moesp (past-output multivariable output error state space model identification) method. Then, calcining belt temperature predictive control system is designed. The control system combines time-delay gain scheduled, output-tracking, recursive subspace adaptive and other methods, and forms the off-line/on-line predictive controller of rotary kiln. At last, MATLAB is applied for simulation, experiments run in constant value tracking and servo tracking situation. Simulation results show its effectiveness and feasibility.

Generalized reliability measures of Kalman filtering for precise point positioning

To deal with the adverse influence of model failures on Kalman filtering (KF) estimation, it is necessary to investigate the generalized reliability theory, including the model failure detection and identification method as well as the separability and reliability theories. Although the generalized reliability theory for the least square has been discussed for many decades, the generalized reliability theory of KF is not widely discussed. Compared with the least square, KF includes not only the measurement model, but also the dynamic model. In KF, the predicted value of the state parameters from the dynamic model is considered as pseudo-measurements and combined with the observed measurements to compose the form of the least square. According to the reliability of the least square, the generalized reliability of KF is derived. Then, the dynamic model failure of precise point positioning is simulated to demonstrate the usage of the generalized reliability theory. The results show that the adverse influence of the dynamic model failure is more severe than that of the measurement model. Moreover, it is recommended that the model failure identification should always be used even if the overall model test passes. It is shown that the derived generalized reliability measures are suitable for the generalized KF estimation.

An Unmanned Helicopter Model Identification Method Based on the Immune Particle Swarm Optimization Algorithm

An unmanned helicopter dynamic model identification method based on immune particle swarm optimization (PSO) algorithm is approved in this paper. In order to improve the search efficiency of PSO and avoid the premature convergence, the PSO algorithm is combined with the immune algorithm. The unmanned helicopter model parameters are coded as particle, the error of flight test and math simulation model is objective function, and the dynamic model of unmanned helicopter is identified. The simulation result shows that the method has high identification precision and can realistically reflect the dynamic characteristics.

Hydraulic turbine governing system identification using T–S fuzzy model optimized by chaotic gravitational search algorithm

Hydraulic turbine governing system (HTGS) is a complicated nonlinear system that controls the frequency and power output of hydroelectric generating unit (HGU). The modeling of HTGS is an important and difficult task, because some components, like hydraulic turbine and governor actuator, are with strong nonlinearity. In this paper, a novel Takagi–Sugeno (T–S) fuzzy model identification method based on chaotic gravitational search algorithm (CGSA) is proposed and applied in the modeling of HTGS. In the proposed method, fuzzy c-regression model clustering algorithm is used to partition the input space and identify the coarse antecedent membership function (MF) parameters at first. And then, a novel CGSA is proposed to search better MF parameters around the coarse results, in which chaotic search has been embedded in the iteration of basic GSA to search and replace the current best solution of GSA. The performance of the proposed fuzzy model identification method is validated by benchmark problems, and the results show that the accuracies of identified models have been improved significantly compared with the other existing models. Finally, the proposed approach has been applied to approximate the dynamic behaviors of HTGS of a HGU in a hydropower station of Jiangxi Province of China. The experimental results show that our approach can identify the HTGS satisfactorily with acceptable accuracy.

Experimental dynamic modelling of peripheral milling with process damping, structural and cutting force nonlinearities

In this paper, an extended dynamic model of peripheral milling process including process damping, structural and cutting force nonlinearities is presented. Cutting forces are described through a third-order polynomial function of chip thickness while a cubic nonlinear function is considered for the structural stiffness. Under stable and regenerative chatter conditions and using Fourier series components, closed form expressions for the nonlinear cutting forces are derived. Parameters of the proposed model are identified through a set of experiments. For this purpose, modal experiments and measurement of cutting forces (at various feed rates) are performed to determine the modal parameters and the coefficients of nonlinear cutting force model. In addition, coefficients of the structural stiffness and process damping are identified through the analysis of experimental and simulated stability lobes diagrams. Simulated stability lobes diagram is constructed based on two approaches: a trial and error (TE) based algorithm and semi-discretization method (SDM). The presented experimental method for model identification can be implemented on any industrial milling process.

On detection of volatility spillovers in overlapping stock markets

Abstract This paper applies a recently proposed structural vector autoregressive model identification method to an established, previously unidentified theoretical model of stock market volatility spillovers. The structural model is identified and can be estimated with the method of maximum likelihood. Volatility spillovers can then be tested with the standard likelihood ratio test. This way our test, unlike the majority of the existing volatility spillover tests, has its foundations firmly in the economic theory. Our test is developed for fully overlapping stock markets. The empirical application of the paper considers stock markets of the eurozone in the years 2010–2011. Evidence of volatility spillovers is found.