Multi-parameter Identification Research Articles

Simultaneous identification of the all parameters for the Lorenz chaotic system

Drawbacks of the adaptive-searching methods, related with the problem of multi-parameter dynamic system identification are explored and highlighted. New approach, based on “moving regression” method is proposed. New approach is a hybrid method; it combines features of the “moving average” method, linear regression method and differential system representation. This combination allows to simultaneously determining complex dynamic system parameters, in spite of its chaotic behavior and measurement errors. New method possibilities are explored via identification process numerical simulation for the Lorenz chaotic system.

Synchronous condenser excitation system parameter identification based on improved artificial bee colony algorithm

The quality of excitation control of the synchronous condenser directly determines the reactive power regulation ability and control support performance of the power grid.Aiming at parameter identification of non-linear links such as limit and saturation in the synchronous condenser excitation system, a parameter identification method of the synchronous condenser excitation system based on improved artificial bee colony algorithm is proposed.Firstly, the order of the phase modulation operation is deduced, and the mathematical model of the synchronous condenser excitation system is established based on it.Then the artificial bee colony algorithm is improved and applied to the parameter identification of the synchronous condenser excitation system. A parameter identification strategy based on sensitivity analysis is proposed for the unstable and multi-solution situation of multi-parameter identification results. Analysis of parameter sensitivity and identification based on parameter sensitivity. Finally, the identification parameters and the overall identification results obtained after sensitivity analysis are compared and analyzed. The results show that the proposed method can effectively identify the parameters of the non-linear link and has strong robustness. It can provide accurate models and parameters for power system stability analysis.

The Parameter Identification of PMSM Based on Improved Cuckoo Algorithm

In view of the multi-parameter identification problem of permanent magnet synchronous motor, a kind of parameter identification method was proposed based on an improved cuckoo search algorithm. Cuckoo search algorithm has the advantages of simple, less parameters, fast convergence etc., but it also has the defects of premature convergence and low computation accuracy. In view of the deficiency of cuckoo search algorithm, the fuzzy reasoning based on the cloud membership was designed to adjust the probability of an alien egg being discovered by host birds and adaptive variable step method was used to adjust the step size of Levy flights. The improved algorithm can accelerate the convergence speed and improve the local and global optimizing ability by increasing the diversity of the population. The multi-parameter identification results of permanent magnet synchronous motor show that the improved cuckoo algorithm can effectively identify the motor parameters, and compared with the traditional cuckoo algorithm, the effectiveness and superior performance are tested.

Parameter Identification and Maximum Power Estimation of Battery/Supercapacitor Hybrid Energy Storage System Based on Cramer–Rao Bound Analysis

This paper presents the analysis, design, and experimental validation of parameter identification of battery/supercapacitor (SC) hybrid energy storage system (HESS) for the purpose of condition monitoring and maximum power estimation. The analytic bounds on the error of battery and SC parameter identification, considering voltage measurement noise, are obtained based on the Fisher information matrix and Cramer–Rao bound analysis. The identification of different parameters requires different signal patterns to ensure high accuracy, rendering tradeoffs in the multiparameter identification process. With an appropriately designed current profile, HESS parameters are identified using recursive least squares with a forgetting factor. The identified parameters are then used to estimate the maximum power capability of the HESS. The maximum power capabilities of the battery and SC are estimated for both 1 and 30 s time horizons. The parameter identification algorithm can be applied to systems including either batteries or SCs when the optimal excitation current can be injected. Experimental validation is conducted on an HESS test-bed, which shows that the proposed algorithm is effective in estimating the HESS maximum power based on appropriate current excitation.

Algorithms for Analyzing the Characteristics of Dendritic Structures

Algorithms for constructing dendrites and analyzing their fractal characteristics are considered. Multi-parameter identification that includes the determination of scaling parameters is proposed. Patterns that determine the relationship between the structure features of dendrites and their diffraction patterns are obtained.

Parameter identification of lithium-ion battery pack for different applications based on Cramer-Rao bound analysis and experimental study

This paper presents an experimental study on the parameter identification of a battery pack, which determines the relationship between identification accuracy and measurement data. Parameter identification of the lithium-ion battery is poor when the input-output data, i.e., the input current and output voltage, is not appropriate. In addition, selection/optimization of an appropriate data set for estimation needs to adapt to different applications. A first-order equivalent circuit model is adopted to model a battery pack, and the identification accuracy is analyzed for both the single-parameter and multi-parameter identification scenarios. It is found that the accuracy of different identification scenarios is influenced by the voltage noise, the current amplitude, and the current frequency. Three experiments using sine waves with different frequencies are then performed to characterize a lithium-ion battery pack. Experimental results show that the current profile with the optimal frequency content achieves the best identification performance. Therefore, it is validated that the identification accuracy can be improved when the current excitation satisfies certain criteria.

Parameter Identification of Capacitive Power Transfer System Based on Spectrum Analysis

Capacitive power transfer (CPT) technology is a newly emerging research focus for EV charging applications. Due to the absence of eddy current loss and light weight of the capacitive coupling metal plates, CPT technology is considered to be a promising alternative to the inductive power transfer (IPT) technology. However, the parameter identification of the CPT system has not been well studied. In this paper, a multi-parameter identification method based on spectral information is proposed, which based on rational fractional fitting algorithm and network synthesis theory. By adding some known parameters to this method, the parameter identification algorithm under constraint is proposed to improve the noise tolerance of the identification results, and at the same time, it can also have better identification accuracy. The experimental results show that the parameter identification algorithm based on spectrum information can effectively identify the parameters of the circuit model of the CPT system, and the identification accuracy is less than 10%.

Multistep and Multiparameter Identification Method for Bridge Cable Systems

Because complex cable systems are widely used in engineering practice, the issues related to parameter identification of complex cable systems are becoming increasingly important. In this study, a universal characteristic frequency equation, considering boundary conditions, various lateral forces, and multiple factors pertaining to cables simultaneously, was established for a class of cable systems installed with intermediate lateral components. The optimization model corresponding to the equation was established to transform the root-finding problem of the complicated transcendental equation into a relatively simple parameter identification and optimization problem. Thereafter, a parameter identification scheme for complicated cable systems was proposed using the particle swarm optimization algorithm. Through experimental tests using a full-scale cable–damper system, a detailed multistep, multiparameter identification program was presented for complex cable systems. The experimental results, achieved effectively using the proposed method, indicate a significant reduction in parameter identification errors and improvement in identification quality.

A Novel Image-based Ultrasonic Test to Map Material Mechanical Properties at High Strain-rates

An innovative identification strategy based on high power ultrasonic loading together with both infrared thermography and ultra-high speed imaging is presented in this article. It was shown to be able to characterize the visco-elastic behaviour of a polymer specimen (PMMA) from a single sample over a range of temperatures and strain-rates. The paper focuses on moderate strain-rates, i.e. from 10 to 200 s−1, and temperatures ranging from room to the material glass transition temperature, i.e. 110∘C. The main originality lies in the fact that contrary to conventional Dynamic Mechanical Thermal Analysis (DMTA), no frequency or temperature sweep is required since the experiment is designed to simultaneously produce both a heterogeneous strain-rate state and a heterogeneous temperature state allowing a local and multi-parametric identification. This article is seminal in nature and the test presented here has good potential to tackle a range of other types of high strain-rate testing situations.

3D Deformation Measurement of Soft Material under Indentation Using Improved Diffraction-Assisted Image Correlation

In inverse finite element-based analysis, complete experimental data collection is critical for multi-parameter identification and physical modeling of all kinds of materials. In this paper, diffraction-assisted image correlation (DAIC) is improved and proposed for the deformation measurement of a soft material under indentation with no blind area. A simple and convenient image-based 3D calibration method was developed, and more accurate formulations for 3D displacement measurement based on a more rigorous imaging model were derived. Using the improved DAIC, a newly developed imaging device with indenter-fixed loading and no blind area is proposed that allows 3D displacements of the whole upper surface of a soft silica gel specimen to be retrieved. The experimental results demonstrate that the proposed method is an accurate, efficient and convenient tool with a simple structure for 3D indentation deformation measurement and illustrate its capabilities to capture deformation in indentation tests with tough testing requirements, such as in situ measurement with limited access (high integration level) and dynamic testing (capturing of synchronously stereo images).

A highly precise frequency-based method for estimating the tension of an inclined cable with unknown boundary conditions

This paper develops a method for precisely determining the tension of an inclined cable with unknown boundary conditions. First, the nonlinear motion equation of an inclined cable is derived, and a numerical model of the motion of the cable is proposed using the finite difference method. The proposed numerical model includes the sag-extensibility, flexural stiffness, inclination angle and rotational stiffness at two ends of the cable. Second, the influence of the dynamic parameters of the cable on its frequencies is discussed in detail, and a method for precisely determining the tension of an inclined cable is proposed based on the derivatives of the eigenvalues of the matrices. Finally, a multiparameter identification method is developed that can simultaneously identify multiple parameters, including the rotational stiffness at two ends. This scheme is applicable to inclined cables with varying sag, varying flexural stiffness and unknown boundary conditions. Numerical examples indicate that the method provides good precision. Because the parameters of cables other than tension (e.g., the flexural stiffness and rotational stiffness at the ends) are not accurately known in practical engineering, the multiparameter identification method could further improve the accuracy of cable tension measurements.

Model reference adaptive control (MRAC)-based parameter identification applied to surface-mounted permanent magnet synchronous motor

ABSTRACTIn this work, a model reference adaptive control-based estimated algorithm is proposed for online multi-parameter identification of surface-mounted permanent magnet synchronous machines. By taking the dq-axis equations of a practical motor as the reference model and the dq-axis estimation equations as the adjustable model, a standard model-reference-adaptive-system-based estimator was established. Additionally, the Popov hyperstability principle was used in the design of the adaptive law to guarantee accurate convergence. In order to reduce the oscillation of identification result, this work introduces a first-order low-pass digital filter to improve precision regarding the parameter estimation. The proposed scheme was then applied to an SPM synchronous motor control system without any additional circuits and implemented using a DSP TMS320LF2812. For analysis, the experimental results reveal the effectiveness of the proposed method.

A selective hybrid stochastic strategy for fuel-cell multi-parameter identification

Abstract The in situ identification of fuel-cell material parameters is crucial both for guiding the research for advanced functionalized materials and for fitting multiphysics models, which can be used in fuel cell performance evaluation and optimization. However, this identification still remains challenging when dealing with direct measurements. This paper presents a method for achieving this aim by stochastic optimization. Such techniques have been applied to the analysis of fuel cells for ten years, but typically to specific problems and by means of semi-empirical models, with an increased number of articles published in the last years. We present an original formulation that makes use of an accurate zero-dimensional multi-physical model of a polymer electrolyte membrane fuel cell and of two cooperating stochastic algorithms, particle swarm optimization and differential evolution, to extract multiple material parameters (exchange current density, mass transfer coefficient, diffusivity, conductivity, activation barriers …) from the experimental data of polarization curves (i.e. in situ measurements) under some controlled temperature, gas back pressure and humidification. The method is suitable for application in other fields where fitting of multiphysics nonlinear models is involved.

Multi-parameter identification and shape reconstruction for unbounded fractal rough surfaces with tapered wave incidence

In this paper, we study the inverse scattering of tapered waves from sound soft surfaces or horizontally polarized electromagnetic waves from perfectly conducting surfaces. By the related Fréchet derivative with respect to a given surface of the solution of direct scattering problems, an efficient algorithm is proposed to reconstruct the parameters of unbounded rough surfaces from a set of scattered field measurements. Tapered wave is introduced to realize the asymptotic truncation for unbounded fractal rough surface. In order to improve the results of reconstruction, multi-angle and multi-frequency incidence strategy has been used. The numerical results show that the method yields satisfactory reconstructions for some rough surface profiles.

Effective methods of parameter identification for creep models with account of III stage

The account of tertiary (third) stage of creep is important for the failure analysis of structures. The correct description of third stage of creep is based on taking into account of damage accumulation process. An identification of the parameters of coupled creep-damage model leads to the optimization problem of finding the global minimum of a functional, having a complex, non-linear structure and a large number of local minimums. The Nelder–Mead method is applied to the solution to the multiparameter identification problem. The effectiveness and robustness of the applied approach are illustrated on examples of mechanical and civil engineering.

GPU-Accelerated Parallel Coevolutionary Algorithm for Parameters Identification and Temperature Monitoring in Permanent Magnet Synchronous Machines

A hierarchical fast parallel co-evolutionary immune particle swarm optimization (PSO) algorithm, accelerated by graphics processing unit (GPU) technique (G-PCIPSO), is proposed for multiparameter identification and temperature monitoring of permanent magnet synchronous machines (PMSM). It is composed of two levels and is developed based on compute unified device architecture (CUDA). In G-PCIPSO, the antibodies (Abs) of higher level memory are selected from the lower level swarms and improved by immune clonal-selection operator. The search information exchanges between swarms using the memory-based sharing mechanism. Moreover, an immune vaccine-enhanced operator is proposed to lead the ${P}$ bests particles to unexplored areas. Optimized parallel implementations of G-PCIPSO algorithm is developed on GPU using CUDA, which significantly speeds up the search process. Finally, the proposed algorithm is applied to multiple parameters identification and temperature monitoring of PMSM. It can track parameter variation and achieve temperature monitoring online effectively. Compared with a CPU-based serial execution, the computational efficiency is greatly enhanced by GPU-accelerated parallel computing technique.

Multi-parameter identification of a two-dimensional water-quality model based on the Nelder–Mead Simplex algorithm

To solve the multi-parameter identification problem of a two-dimensional river water-quality model, a new parameter identification method based on the Nelder–Mead Simplex algorithm coupled with the alternating direction implicit method has been constructed to determine hydraulic and water-quality parameters such as the longitudinal dispersion coefficient, the transverse mixing coefficient, and the pollutant degradation coefficient. Moreover, the influences of observation noise, observation location, and sampling frequency on the identified parameters were discussed for the given model. The method was validated using three numerical cases (two steady state and one dynamic), and one field experiment. The computational results indicated that the model provided good identification precision and showed good anti-noise capability. The longitudinal distribution of observed points made it possible to identify the contributions of the degradation coefficient K and the transverse distribution to the identification of the transverse dispersion coefficient Ey. Sampling frequency has a strong influence on the accuracy of the identified parameters. Generally, the higher the sampling frequency, the higher will be the accuracy obtained, but the convergence rate may be slow and the computational time lengthy. Therefore, when dealing with practical problems, a reasonable balance should be sought between the amount of calculation required and the parameter estimation accuracy.

Multiparameter identification of a lossy fluid-like object from its transient acoustic response

A transient acoustic wave multiparameter inverse problem is studied. The aim is to simultaneously retrieve three constitutive parameters and one geometrical parameter of a lossy fluid-like cylinder by nonlinear full-wave inversion of synthetic data. Contrary to most publications treating this subject, it is assumed herein that the retrieval model is not identical to the data model, this being so because some of the parameters (priors) in the retrieval model are different (by the simple fact of being more or less unknown) from their true counterparts in the data model. This so-called model discordance, which is the usual situation in real-world inverse problems, is a source of errors for the retrieval of the other parameters. Moreover, it is a source of non-uniqueness and instability, a fact revealed by the employment of a properly-tuned Nelder-Mead downhill Simplex optimization algorithm, and which requires a second-order regularization for its resolution. Retrieval errors as a function of prior uncertainty are computed, and found to be large, for various model discordance scenarios involving one or two uncertain priors, for data with and without noise.

Parameter identification of river water quality models using a genetic algorithm

For solving the multi-parameter identification problem of a river water quality model, analytical methods for solving a river water quality model and traditional optimization algorithms are very difficult to implement. A new parameter identification model based on a genetic algorithm (GA) coupled with finite difference method (FDM) was constructed for the determination of hydraulic and water quality parameters such as the longitudinal dispersion coefficient, the pollutant degradation coefficient, velocity, etc. In this model, GA is improved to promote convergence speed by adding the elite replacement operator after the mutation operator, and FDM is applied for unsteady flows. Moreover the influence of observation noise on identified parameters was discussed for the given model. The method was validated by two numerical cases (in steady and unsteady flows respectively) and one practical application. The computational results indicated that the model could give good identification precision results and showed good anti-noise abilities for water quality models when the noise level ≤10%.

Multi-parameter identification of power line communication channel model based on matching pursuit

The over-complete atom dictionary is designed on the basis of typical multi-path transmission function of power line communication (PLC) channel, and matching pursuit (MP) algorithm is used for multi-parameter identification of the channel. First, the sampling data were obtained from the measured result of low-voltage PLC channel within the range from 300kHz to 6000kHz, which help us to develop a model for the PLC channel. Then, in order to reduce the computation complexity of MP algorithm to identify the multi-parameter of PLC, genetic algorithm (GA) is applied to effectively search for the best atom at each step of MP in the dictionary of atoms. From the multi-parameter identification result with three or four paths channel models, it demonstrates that the combined algorithm based on MP and GA can acquire more evaluation precision of PLC channel than that of GA and particle swarm optimization (PSO) algorithm respectively. Meanwhile, the more numbers of channel paths in the process of multi-parameter identification, the better will be the curve fitting result compared to the experiment. The experimental results have validated the efficiency of the combined MP–GA algorithm that proposed in this paper.