Proposed Identification Algorithm Research Articles

A Novel Online Multivariate Identification for Autotuning Speed Control in PMSM Drives

A novel online algorithm to identify the moment of inertia, viscous friction coefficient, and load torque of PMSM (Permanent Magnet Synchronous Motor) drives and a distinctive autotuning speed control scheme are presented. The proposed identification algorithm does not require motors run in a particular trajectory and only needs a short identification time. A Luenberger speed observer is introduced to eliminate noises which are generated by the detection of position signal and to improve the accuracy of identified parameters. Parameters of the speed controller are optimized by analyzing the mathematical model of the system and the formula of the PI controller. Compared to a standard recursive least squares method (RLSM) and traditional PI algorithm, the effectiveness of the proposed identification algorithm and autotuning speed control scheme are validated through simulations and experiments.

A Variational Bayesian Approach to Robust Identification of Switched ARX Models.

A variational Bayesian approach to robust identification of switched auto-regressive exogenous models is developed in this paper. By formulating the problem of interest under a full Bayesian identification framework, the number of local-models can be determined automatically, while accounting for the uncertainty of parameter estimates in the overall identification procedure. A set of significance coefficients is used to assign proper importance weights to local-models. By maximizing the marginal likelihood of the identification data, insignificant local-models will be suppressed and the optimal number of local-models can be determined. Considering the fact that the identification data may be contaminated with outliers, t distributions with adjustable tails are utilized to model the contaminating noise so that the proposed identification algorithm is robust. The effectiveness of the proposed Bayesian approach is demonstrated through a simulated example as well as a detailed industrial application.

Photogrammetric detection technique for rotor blades structural characterization

This paper describes an innovative use of photogrammetric detection techniques to experimentally estimate structural/inertial properties of helicopter rotor blades. The identification algorithms for the evaluation of mass and flexural stiffness distributions are an extension of the ones proposed by Larsen, whereas the procedure for torsional properties determination (stiffness and shear center position) is based on the Euler-Prandtl beam theory. These algorithms rely on measurements performed through photogrammetric detection, which requires the collection of digital photos allowing the identification of 3D coordinates of labeled points (markers) on the structure through the correlation of 2D pictures. The displacements are evaluated by comparing the positions of markers in loaded and reference configuration. Being the applied loads known, the structural characteristics can be directly obtained from the measured displacements. The accuracy of the proposed identification algorithms has been firstly verified by comparison with numerical and experimental data, and then applied to the structural characterization of two main rotor blades, designed for ultra-light helicopter applications.

Structural identification with incomplete output‐only data and independence of measured information for shear‐type systems

SummaryStructural identification is the inverse problem of estimating physical parameters of a structural system from its vibration response measurements. Incomplete instrumentation and ambient vibration testing generally result in incomplete and arbitrarily normalized measured modal information, often leading to an ill‐conditioned inverse problem and non‐unique identification results. The identifiability of any parameter set of interest depends on the amount of independent available information. In this paper, we consider the identifiability of the mass and stiffness parameters of shear‐type systems in output‐only situations with incomplete instrumentation. A mode shape expansion‐cum‐mass normalization approach is presented to obtain the complete mass normalized mode shape matrix, starting from the incomplete non‐normalized modes identified using any operational modal analysis technique. An analysis is presented to determine the minimum independent information carried by any given sensor set‐up. This is used to determine the minimum necessary number and location of sensors from the point of view of minimum necessary information for identification. The different theoretical discussions are illustrated using numerical simulations and shake table experiments. It is shown that the proposed identification algorithm is able to obtain reliably accurate physical parameter estimates under the constraints of minimal instrumentation, minimal a priori information, and unmeasured input. The sensor placement rules can be used in experiment design to determine the necessary number and location of sensors on the monitored system. John Wiley & Sons, Ltd.

Detection and classification from electromagnetic induction data

In this paper we introduce an efficient algorithm for identifying conductive objects using induction data derived from eddy currents. Our method consists of first extracting geometric features from the induction data and then matching them to precomputed data for known objects from a given dictionary. The matching step relies on fundamental properties of conductive polarization tensors and new invariance properties introduced in this paper. A new shape identification scheme is developed and tested in numerical simulations in the presence of measurement noise. Resolution and stability properties of the proposed identification algorithm are investigated.

Parametric estimation of ship maneuvering motion with integral sample structure for identification

This documentation presents the parametric identification modeling of ship maneuvering motion with integral sample structure for identification (ISSI) and Euler sample structure for identification (ESSI) based on least square support vector machines (LS-SVM), where ISSI is used for the construction of in–out sample pairs. By using Mariner Class Vessel, the sample dataset are obtained from 15°/15° zigzag maneuvering simulation based on Abkowitz model. By analyzing the simulation data including rudder angle, surge velocity, sway velocity, yaw rate and so forth, the hydrodynamic derivatives in Abkowitz model are all identified. The validation of the proposed identification algorithm is verified by the high precisions of the identified hydrodynamic derivatives and maneuvering prediction results. The comparison is also conducted between the proposed ISSI and the conventional Euler sample structure for identification (ESSI), and the experimental results shows that ISSI is much more appropriate for parametric identification modeling of ship maneuvering motion.

Adaptive Nonlinear Systems Identification via Discrete Multi-Time Scales Dynamic Neural Networks

AbstractIn this paper, we extend our previous results on continuous multi-time scales dynamic neural networks identification to the discrete domain. A robust on-line identification algorithm is proposed for nonlinear systems identification via discrete multi-time scales dynamic neural networks. The main contribution of the paper is that the input-to-state stability (ISS) approach is used to tune the weights of the discrete multi-time scales dynamic neural networks in the sense of L1. The commonly used robustifying techniques, such as dead-zone or s-modification in the weight tuning, are not necessary for the proposed identification algorithm. The stability of the proposed identifier is proved by Lyapunov function and ISS theory. Two examples are given to demonstrate the correctness of the theoretical results.

Dynamic Model Identification for 6-DOF Industrial Robots

A complete and systematic procedure for the dynamical parameters identification of industrial robot manipulator is presented. The system model of robot including joint friction model is linear with respect to the dynamical parameters. Identification experiments are carried out for a 6-degree-of-freedom (DOF) ER-16 robot. Relevant data is sampled while the robot is tracking optimal trajectories that excite the system. The artificial bee colony algorithm is introduced to estimate the unknown parameters. And we validate the dynamical model according to torque prediction accuracy. All the results are presented to demonstrate the efficiency of our proposed identification algorithm and the accuracy of the identified robot model.

Data filtering based parameter estimation algorithms for multivariable Box-Jenkins-like systems

This paper proposes an auxiliary model based hierarchical least squares algorithm for multivariable Box-Jenkins-like systems using the hierarchical identification principle. To improve the computational efficiency, a multivariable system is decomposed into two subsystems by using the data filtering technique. Furthermore, this paper presents a data filtering based auxiliary model hierarchical least squares algorithm for multivariable Box-Jenkins-like systems. The simulation example shows that the proposed identification algorithms are effective.

Structure and parameter identification for Bayesian Hammerstein system

In this paper, we consider the structure and parameter identification problem for Bayesian Hammerstein system. A structure identification algorithm is proposed, in which the system order, system parameters and regularization parameters are all unknown in the considered system. The joint posterior distribution of system parameters and the value of basis functions $$k$$ are obtained via sampling theory. The proposed identification algorithm is based on the reversible jump Markov chain Monte Carlo method. There are two main characteristics of the algorithm: (i) By using the birth move and death move strategy, the parameter $$k$$ is searched quickly in the number of basis functions space until the suitable value of $$k$$ is found. (ii) The distributions of system parameters are changed with the value of $$k$$ in the Bayesian framework, and the parameters are successfully found after the value of $$k$$ is stable. Two examples are provided to show the effectiveness of the proposed algorithm. The performances of the algorithm are validated with the results of statistical analyses including parameter estimate error, MSE, NRMSE, MAD, Theils inequality coefficient, etc.

Bias-compensation-based least-squares estimation with a forgetting factor for output error models with white noise

In this paper, the bias-compensation-based recursive least-squares (LS) estimation algorithm with a forgetting factor is proposed for output error models. First, for the unknown white noise, the so-called weighted average variance is introduced. With this weighted average variance, a bias-compensation term is first formulated to achieve the bias-eliminated estimates of the system parameters. Then, the weighted average variance is estimated. Finally, the final estimation algorithm is obtained by combining the estimation of the weighted average variance and the recursive LS estimation algorithm with a forgetting factor. The effectiveness of the proposed identification algorithm is verified by a numerical example.

Introducing $$g$$ g -normal fuzzy relational models

In this paper, a harmonious family of fuzzy relational models (FRMs) called $$g$$g-normal FRMs is introduced. By selecting a value for the scalar parameter $$g$$g, a member of this family is determined for which all parts of the model are configured accordingly. In this regard, a specific configuration for FRMs as well as an appropriate derivative-based iterative identification procedure are proposed. While preserving the high modeling capability of FRMs, the proposed model configuration along with the proposed identification algorithm alleviate both the lack of analyzability and the possible existence of hard conflicts between the rules in the rule-base. Furthermore, high relative errors in output computations are prevented using the proposed scheme. The modeling scheme is applied finally to the Box---Jenkins gas furnace benchmark problem as well as a new classification problem. The results are verified by simulation.

On Identifying Envelop Type Nonlinear Output Error Takagi-Sugeno Fuzzy Models for Dynamic Systems with Uncertainties

In modeling of a stochastic nonlinear dynamic system from input-output data, it may be of interest to model uncertainty in the underlying system besides predicting a most likely or average response of the system. Due to stochasticity in the system behavior, the data obtained for identification can be considered as one realization of the underlying stochastic phenomenon. In order to effectively deal with the identification of such systems, it may be advantageous to repeat the identification experiment multiple times under similar conditions. The multiple input-output time series generated in this way thus contain information about stochastic variations within the system. This paper presents one of the possible approaches to effectively deal with identification in such scenario in the framework of Nonlinear Output Error (NOE) Takagi-Sugeno (TS) fuzzy models. Based on extended Chebyshev's inequality for finite samples, the lower and upper boundaries of the output time-series are obtained using (1-α) confidence interval (envelops of the response). The proposed identification algorithm provides a model for predicting the most likely value as well as the boundary models for predicting the envelops of the output signal. The experimental results for an electro-mechanical throttle shows the applicability and validity of the proposed approach.

Numerical and Experimental Investigation on Parameter Identification of Time-Varying Dynamical System Using Hilbert Transform and Empirical Mode Decomposition

This paper proposes an approach to identifying time-varying structural modal parameters using the Hilbert transform and empirical mode decomposition. Definition of instantaneous frequency and instantaneous damping ratio based on Hilbert transform for single-degree-of-freedom (SDOF) system is first introduced. The following is the definition of Hilbert damping spectrum from which the time-varying damping ratio of multi-degree-of-freedom (MDOF) system can be calculated. Identification procedures for both instantaneous frequency and damping ratios based on their definition are then introduced. Applicability of the proposed identification algorithm has been validated through several numerical examples. The instantaneous frequency and damping ratios of SDOF system under free vibration and under sinusoidal and white noise excitation have been identified. The proposed method is also applied to MDOF system with slow and sudden changing structural parameters. The results demonstrate that when the system modal parameters are slowly changing, the instantaneous frequency could be easily and well identified with satisfied accuracy for all cases. However, the instantaneous damping ratio could be extracted only when the system is lightly damped. The damping results are better for free vibration situation than for the forced vibration cases. It is also shown that the suggested method can easily track the abrupt change of system modal parameter under free vibration. The proposed method is then applied to a 12-story short-lag shear wall structure model tested on a shaking table. The instantaneous dynamic properties of the structure were identified and were then introduced as known parameters into a finite element model. Comparisons with the numerical results using constant structural parameters demonstrate that the calculated structural responses using the identified time-varying parameters are much closer to the experimental results.

Shape Identification and Classification in Echolocation

This paper introduces a new approach to shape perception in wave imaging. It aims at proposing an efficient shape identification and classification algorithm in echolocation. This approach is based on first extracting geometric features from the reflected waves and then matching them with precomputed ones associated with a dictionary of targets. The construction of such frequency-dependent shape descriptors is based on some important properties of scattering coefficients and new invariants. Stability and resolution of the proposed identification algorithm with respect to measurement noise and limited-view aspect are analytically and numerically quantified.

Yeast Identification Algorithm Based on Use of the Vitek MS System Selectively Supplemented with Ribosomal DNA Sequencing: Proposal of a Reference Assay for Invasive Fungal Surveillance Programs in China

Sequence analysis of the internal transcribed spacer (ITS) region was employed as the gold standard method for yeast identification in the China Hospital Invasive Fungal Surveillance Net (CHIF-NET). It has subsequently been found that matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) is potentially a more practical approach for this purpose. In the present study, the performance of the Vitek MS v2.0 system for the identification of yeast isolates collected from patients with invasive fungal infections in the 2011 CHIF-NET was evaluated. A total of 1,243 isolates representing 31 yeast species were analyzed, and the identification results by the Vitek MS v2.0 system were compared to those obtained by ITS sequence analysis. By the Vitek MS v2.0 system, 96.7% (n = 1,202) of the isolates were correctly assigned to the species level and 0.2% (n = 2) of the isolates were identified to the genus level, while 2.4% (n = 30) and 0.7% (n = 9) of the isolates were unidentified and misidentified, respectively. After retesting of the unidentified and misidentified strains, 97.3% (n = 1,209) of the isolates were correctly identified to the species level. Based on these results, a testing algorithm that combines the use of the Vitek MS system with selected supplementary ribosomal DNA (rDNA) sequencing was developed and validated for yeast identification purposes. By employing this algorithm, 99.7% (1,240/1,243) of the study isolates were accurately identified with the exception of two isolates of Candida fermentati and one isolate of Cryptococcus gattii. In conclusion, the proposed identification algorithm could be practically implemented in strategic programs of fungal infection surveillance.

Discrete-Time Parameter Identification of a Surface-Mounted Permanent Magnet Synchronous Machine

A novel online discrete-time parameter identification algorithm suitable for surface-mounted permanent magnet synchronous machines (SPMs) is presented in this paper. It is developed by means of the model reference adaptive system technique and the Popov Hyperstability Criterion in order to identify SPM discrete-time model parameters. In particular, good accuracy of discrete-time parameters is required by digital control systems, particularly by predictive control algorithms, which present a low robustness against parameter mismatches. Hence, an extensive simulation study is first carried out in the Matlab Simulink environment with the aim of testing the effectiveness and robustness of the proposed identification algorithm against inverter unidealities. Then, the proposed identification procedure is experimentally validated on a predictive controlled radial-flux SPM, driven by a field programmable gate arrays control board.

Target Identification Using Dictionary Matching of Generalized Polarization Tensors

The aim of this paper is to provide a fast and efficient procedure for (real-time) target identification in imaging based on matching on a dictionary of precomputed generalized polarization tensors (GPTs). The approach is based on some important properties of the GPTs and new invariants. A new shape representation is given and numerically tested in the presence of measurement noise. The stability and resolution of the proposed identification algorithm is numerically quantified. We compare the proposed GPT-based shape representation with a moment-based one.

Generalized adaptive comb filters/smoothers and their application to the identification of quasi-periodically varying systems and signals

The problem of both causal and noncausal identification of linear stochastic systems with quasi-harmonically varying parameters is considered. The quasi-harmonic description allows one to model nonsinusoidal quasi-periodic parameter changes. The proposed identification algorithms are called generalized adaptive comb filters/smoothers because in the special signal case they reduce down to adaptive comb algorithms used to enhance or suppress nonstationary harmonic signals embedded in noise. The paper presents a thorough statistical analysis of generalized adaptive comb algorithms, and demonstrates their statistical efficiency in the case where the fundamental frequency of parameter changes varies slowly with time according to the integrated random-walk model.

Parameter and differentiation order estimation in fractional models

This paper deals with continuous-time system identification using fractional differentiation models. An adapted version of the simplified refined instrumental variable method is first proposed to estimate the parameters of the fractional model when all the differentiation orders are assumed known. Then, an optimization approach based on the use of the developed instrumental variable estimator is presented. Two variants of the algorithm are proposed. Either, all differentiation orders are set as integral multiples of a commensurate order which is estimated, or all differentiation orders are estimated. The former variant allows to reduce the number of parameters and can be used as a good initial hit for the latter variant. The performances of the proposed approaches are evaluated by Monte Carlo simulation analysis. Finally, the proposed identification algorithms are used to identify thermal diffusion in an experimental setup.