Multi-parameter Identification Research Articles

Reverse Nonlinear Sparrow Search Algorithm Based on the Penalty Mechanism for Multi-Parameter Identification Model Method of an Electro-Hydraulic Servo System

Aiming at the multi-parameter identification problem of an electro-hydraulic servo system, a multi-parameter identification method based on a penalty mechanism reverse nonlinear sparrow search algorithm (PRN-SSA) is proposed, which transforms the identification problem of a non-linear system into an optimization problem in a high-dimensional parameter space. In the initial stage of the sparrow search algorithm (SSA), the population distribution is not uniform, and the optimization process is easily disturbed by the local optimal solution. First, adopting a reverse learning strategy increases the exploratory nature of individuals in a population, improves population diversity, and prevents premature maturity. Subsequently, a flexible strain mechanism is provided through the nonlinear convergence factor, adaptive weight factor, and golden sine and cosine factor. The introduction of a nonlinear factor fully balances the global search and local development abilities of the algorithm. Finally, a punishment processing mechanism is developed for vigilantes while retaining the population, providing a suitable search scheme for individuals beyond the boundary, and making full use of the value of each sparrow individual. The effectiveness of each improved strategy is verified through simulation experiments with 23 benchmark functions, and the improved algorithm exhibits better robustness. The results of the model parameter identification of the electro-hydraulic servo system show that the method has a high fitting accuracy between the identification model data and the experimental data, and the fitting degree of the identification model exceeds 97.54%, which further verifies the superiority of the improved algorithm and the effectiveness of the proposed identification strategy.

Multiparametric Identification of Favorable Regions for Wind or Solar Generation in the State of Pernambuco

The capacity for renewable energy generation, particularly solar and wind, tends to grow over the years. However, locating regions favorable to the development of solar and wind generation is not trivial and is subject to errors due to the social, environmental, economic, political and administrative aspects of the regions. Therefore, this article proposes an algorithm based on digital image processing, capable of performing a variable weight multi-parameter evaluation and quantifying how favorable a given territorial region is for the implantation of wind or solar plants. Maps of the region under analysis are taken as input information. The region taken as a case study is the Brazilian state of Pernambuco, due to the access to data related to the evaluated characteristics: solar radiation, solar insolation, precipitation, air humidity, temperature, electrogeography, demographic density, environmental protection areas, slope, hydrography, wind speed, urban spot and the presence of aerodromes. It was possible to list for each type generation (wind or solar, exclusively), the 15 most favorable regions for the implementation plants. Furthermore, the proposed technique presents a practical indifference to the territorial extension of the evaluated region, which can comprehend a micro-region or an entire continent, requiring only that sufficiently detailed maps of the evaluated characteristics be provided.

An integrated calibration technique for variable-boresight three-dimensional imaging system

There is an emerging need for multiview stereo systems achieved with high accuracy, wide coverage, and flexible perspective in spatially-limited vision and close-range photogrammetry. Accurate and efficient calibration is a critical prerequisite that is always performed to ensure high-performance object reconstruction and situational awareness. This paper proposes an integrated calibration technique based on rigorous modeling for the variable-boresight three-dimensional imaging system composed of a single camera and a wedge prism. A unified self-calibration framework which considers the misalignment between camera and prism is systematically demonstrated such that all involved parameters are estimated by minimizing the statistical discrepancy between model prediction and reference information. Moreover, a novel approach is presented to construct calibration reference through adaptive generation and flexible transfer. Experiments have validated that the proposed technique can offer a viable solution to multi-parameter identification for the imaging system, which therefore contributes to the recovery of any spatial object with competitive geometric accuracy and satisfactory robustness to potential error sources.

Multiparameter Identification of Permanent Magnet Synchronous Motor Based on Model Reference Adaptive System—Simulated Annealing Particle Swarm Optimization Algorithm

The accurate identification of permanent magnet synchronous motor (PMSM) parameters is the basis for high-performance drive control. The traditional PMSM multiparameter identification method experiences problems with the uncertainty of the identification results and low identification accuracy due to the under-ranking of the mathematical model of motor control. A multiparameter identification of PMSM based on a model reference adaptive system and simulated annealing particle swarm optimization (MRAS-SAPSO) is proposed here. The algorithm first identifies the electrical parameters of the PMSM (stator winding resistance R, cross-axis inductance L, and magnetic linkage ψf) by means of the model reference adaptive system method. Second, the result is used as the initial population in particle swarm optimization identification to further optimize and identify the electrical and mechanical parameters (moment of inertia J and damping coefficient B) in the motor control system. Additionally, in order to avoid problems such as premature convergence of the particle swarm in the optimization search process, the results of the adaptive simulated annealing algorithm to optimize multiparameter identification are introduced. The simulation experiment results show that the five identification parameters obtained by the MRAS-SAPSO algorithm are highly accurate and stable, and the errors between them and the real values are below 2%. This also verifies the effectiveness and reliability of this identification method.

Research on the uniqueness and algorithm of multi-parameter identification in coupling thermal acoustic and solid problem

<p indent="0mm">In the coupled thermal-acoustic solid field, we consider the identification problem of initial boundary value from the boundary measurements of temperature and ultrasonic propagation time to reconstruct the boundary heat flux and thickness of a solid structure simultaneously. Through rigorous theoretical analysis, the uniqueness of the simultaneous identification is demonstrated, which provides theoretical support for engineering applications. By reformulating an inverse problem as an optimization problem and deriving the adjoint equation, we propose an efficient alternating iteration algorithm for solving the inverse problem. Rigorous convergence analysis of the proposed algorithm is performed. Finally, the reliability and feasibility of the algorithm are verified via numerical examples, and the algorithm’s accuracy is verified by comparing it with a single-parameter identification algorithm that only identifies heat flux.

Multi-parameter identification of gratings measurement by Experimental Ray Tracing

A simple method for measuring grating groove density as well as its position and orientation is proposed based on the idea of ERT (Experimental Ray Tracing). Conventional methods only measure grating groove density with accuracy limited by its rotary stage and goniometer. The method proposed in the paper utilizes linear guides which could be calibrated to much higher accuracy. It is applicable to gratings of arbitrary surface profile or mosaic of a group of various gratings. Various measurement error sources are simulated by the Monte Carlo method and the results show high accuracy capability of grating parameters identification. A verification testing is performed. The accuracy dependency on main configuration parameters is evaluated. A method to expand measurement range by double wavelength is also discussed.

Research on Automatic Control Method of Motor Current in Distribution Network Based on Bionic Algorithm

Due to the problems of multi-parameter identification and processing in traditional approach of current-predicting, it is difficult to control PMSM in practical complex environments. In this paper, a predictive current control method based on improved extended observer is proposed, whose mathematical model based on extended state observer is also established. Because of the problem of multi-parameter setting in ESO, the flower pollination algorithm is used to adjust the parameters with the fitness function of minimum absolute error of ESO input and output. To verify the stability of PMSM system based on predicted current control with improved ESO, MATLAB/SIMULINK is used to build the simulation model. And the results show that the control strategy can predict the accurate current ofdq, and the PMSM based on this method can output the desired current, determining the stable operation of the system.

Parameter identification of permanent magnet synchronous AC motor based on spectral method

This paper establishes a mathematical model with random terms for a permanent magnet synchronous AC motor, which is a nonlinear system using d - axis current, q - axis current and the rotor electric angular speed as state variables. The numerical solution obtained by the Euler-Maruyama method is used as the measurement data. Aiming at the parameter identification of the system, a step-by-step identification method based on the spectral method discretize the system first and then using the least squares is proposed. This method is used to identify multiple parameters of the motor in the same model. In step-by-step identification, firstly by fixing the motor speed, the system is transformed into a linear system, which is used to estimate the resistance, inductance and flux linkage. After that, the speed is not fixed, for the electrical parameters are known, we can identify damping and inertia by using mechanical equations. Finally, the experimental results show that the relative errors of the parameters identified by the proposed method are smaller, which shows the effectiveness of this method for multi-parameter identification.

A Method for Parameter Identification of Composite Beam Piezoelectric Energy Harvester.

This paper proposes a parameter identification method for the multiparameter identification study of the linear–arch composite beam piezoelectric energy harvester. According to the voltage response characteristics of the system under short-circuit conditions, the mechanical equation is solved by transient excitation, combined with the backbone curve theory and logarithmic attenuation method, to obtain the system’s linear damping, linear stiffness, and nonlinear stiffness. According to the voltage response characteristics of the system under open-circuit conditions, combined with the electrical equations, the system electromechanical coupling coefficient and equivalent capacitance coefficient are obtained; numerical simulation results show that the identification parameters have good accuracy. Finally, an experimental platform was built for verification, and the results show that the method has high accuracy and practicability.

HIGH-PERFORMANCE MODELING, IDENTIFICATION AND ANALYSIS OF HETEROGENEOUS ABNORMAL NEUROLOGICAL MOVEMENT’S PARAMETERS BASED ON COGNITIVE NEURO FEEDBACK-INFLUENCES

The technique is based on a hybrid model of the neuro-system (Brain cortex nodes and tremor-object), which describes on the basis of wave signal propagation the state and behavior of tremor-objects T, namely the segmental de-scription of 3D elements of trajectories of anormal neurological movements of the studied tremor-objects (limb of the hand) taking into account the matrix of cogni-tive influences of groups of cortex neuro-nodes. The rapid analytical solution of the model as a vector function that describes the 3D elements of the trajectories at each movements segment are constructed using the hybrid integral Fourier’s transformations and hybrid spectral function. The main element of the solution is the adaptive infuences matrix that determines the state parameters of the action of certain groups of brain neuro- cortex. Models and methods of multivariable identification are being developed to investigate their neuro-feedback, which suggest high-speed parallel computations on multicore computers. This model-ing technology consider as a scientific basis for designing inelidgence information systems of the quality medical diagnostic i of critical neurological diseases.Key words: Computer simulation, Software system, High-performance compu-ting, Tremor diseases, Modeling of objects and processes, Multi-parameter iden-tification.

Composite control for disturbed direct-driven surface-mounted permanent magnet synchronous generator with model prediction strategy

In order to obtain the best power in the wind energy conversion system (WECS) of the direct-driven surface-mounted permanent magnet synchronous generator (SPMSG), active disturbance rejection control (ADRC) is introduced to track the motor speed in real time. The control algorithm provides a new design concept and an inherent robust controller component that requires very little system information. Aiming at the problem of system parameter mutation caused by internal factors and external environment changes, an adaptive controller with multi parameter identification is designed, and the disturbance caused by parameter changes is compensated in real time. The model predictive current control (MPC) technology for the sudden change of external environment is designed to accelerate the response speed of the current loop, so as to weaken the estimation of the current disturbance by the active disturbance rejection controller, and make the speed estimation more accurate. Simulation results show that the proposed control strategy is effective and satisfactory.

Методы моделирования и идентификация параметров неоднородных анормальных неврологических движений многокомпонентных нейробиосистемах с когнитивными обратными связями

Разработаны основы математического моделирования и идентификации параметров неоднородных анормальных неврологических движений (АНР) в многокомпонентных нейробиосистемах с обратными когнитивными связями. На основе развитых авторами методов интегральных превращений и спектрального анализа для неоднородных сред предложен новый подход к построению гибридных моделей распространения волнового сигнала, описывающего нежелательные дрожания конечности руки пациента (Т-объекта) в результате самопроизвольного сокращения скелетных мышц. за счет когнитивных воздействий отдельной группы нейронных узлов коры головного мозга (КГМ). Разработана гибридная модель нейробиосистемы, описывающая состояние и поведение Т-объектов, а именно посегментное описание 3D-элементов траекторий АНР Т-объекта с учетом матрицы когнитивных воздействий групп нейроузлов КГМ. На основе гибридных интегральных преобразований Фурье получено высокоскоростное аналитическое векторное решение модели, описывающей элементы траекторий на каждом АНР-сегменте. Предложена новая методика вычисления гибридной спектральной функции, спектральных значений и матрицы когнитивных воздействий нейроузлов КГМ, определяющих гибридное интегральное преобразование построения решения. Сформулированы и решены новые неклассические задачи многопараметрической идентификации нейросистем с обратной связью в неоднородных средах на основе минимизации функционала-невязки между траекториями наблюдения и их модельными аналогами. Построены высокопроизводительные алгоритмы идентификации амплитудно-частотных характеристик систем обратной связи для компонентной оценки влияния когнитивных обратных связей, позволяющих распараллелировать вычисления для многоядерных компьютеров. Выполнено компьютерное моделирование и идентификацию АНР-траекторий нейросистем обратной связи.

Parameter identification of capacitive transfer system based on Fitting Algorithm

At present, in the wireless power transfer (WPT) of electric vehicle (EV), inductive power transfer (IPT) is still the main method. However, capacitive power transfer (CPT) has the advantages of no eddy current loss of coupling mechanism and light weight, which make it become a new research hotspot in the charging application of EV. But CPT system has high working frequency and sensitive parameters, therefore, in practical application, it is necessary to identify important parameters such as coupling capacitance and load. But at present, there are few identification methods for parameters of CPT. In this paper, a multi-parameter identification algorithm is proposed for capacitive wireless charging technology. Based on Vector Fitting algorithm and network synthesis principle, multi-parameter identification is realized. But in order to be widely used in practice, some parameters in circuit model are considered as known constraint. This can ensure that the proposed algorithm can not only realize multi-parameter identification, but also ensure the identification accuracy of system parameters in practical application.

Frequency-based tension assessment of an inclined cable with complex boundary conditions using the PSO algorithm.

The frequency-based method is the most commonly used method for measuring cable tension. However, the calculation formulas for the conventional frequency-based method are generally based on the ideally hinged or fixed boundary conditions without a comprehensive consideration of the inclination angle, sag-extensibility, and flexural stiffness of cables, leading to a significant error in cable tension identification. This study aimed to propose a frequency-based method of cable tension identification considering the complex boundary conditions at the two ends of cables using the particle swarm optimization (PSO) algorithm. First, the refined stay cable model was established considering the inclination angle, flexural stiffness, and sag-extensibility, as well as the rotational constraint stiffness and lateral support stiffness for the unknown boundaries of cables. The vibration mode equation of the stay cable model was discretized and solved using the finite difference method. Then, a multiparameter identification method based on the PSO algorithm was proposed. This method was able to identify the tension, flexural stiffness, axial stiffness, boundary rotational constraint stiffness, and boundary lateral support stiffness according to the measured multiorder frequencies in a synchronous manner. The feasibility and accuracy of this method were validated through numerical cases. Finally, the proposed approach was applied to the tension identification of the anchor span strands of a suspension bridge (Jindong Bridge) in China. The results of cable tension identification using the proposed method and the existing methods discussed in previous studies were compared with the on-site pressure ring measurement results. The comparison showed that the proposed approach had a high accuracy in cable tension identification. Moreover, the synchronous identification of the flexural stiffness, axial stiffness, boundary rotational constraint stiffness, and boundary lateral support stiffness was highly beneficial in improving the results of cable tension identification.

Real-Time Multiparameter Identification of a Salient-Pole PMSM Based on Two Steady States

Real-time multiparameter identification has been widely investigated in relation to high-performance control and fault diagnosis of salient-pole permanent magnet synchronous motors (PMSMs). However, it is rank-deficient for simultaneously estimating flux, resistance, and dq-axis inductances based on one steady state under maximum torque per ampere (MTPA) control, which will cause the ill-convergence problem in the results. This paper proposes a new method to solve the rank deficiency problem in the multiparameter identification of salient-pole PMSMs in systems where the motor working conditions do not change frequently. For this type of system, a second steady state is constructed in order to meet the full-rank conditions for multiparameter identification and minimize the torque ripple. Furthermore, in order to reduce the influence of inductance variations, a better shift direction from the first steady state to the second is ensured based on the analysis of the theoretical error. Simulation and experimental results show that the proposed method demonstrates good identification performance.

Multiparametric Identification of Several Thermophysical Characteristics by Solving the Internal Inverse Heat Conduction Problem

Multiparametric Identification of Several Thermophysical Characteristics by Solving the Internal Inverse Heat Conduction Problem

Metabolomics in equine sport and exercise.

metabolomics is the high-throughput, multiparametric identification and classification of hundreds of low molecular weight metabolites in a biological sample. Ultimately, metabolites are the downstream readouts of cellular signalling, transcriptomic and proteomic changes that can provide a comprehensive view of tissue and organismal phenotype. The popularity of metabolomics in human sport and exercise has been gaining over the past decade and has provided important insights into the energetic demands and mechanistic underpinnings of exercise and training. To the contrary, metabolomics in the field of equine exercise physiology is lagging despite the horse's superior aerobic and muscular capabilities, as well as its prominence in competitive sport. As such, this narrative review aims to describe metabolomics, its routine implementation, the various analytical methods applied and the state of its use in the equine athlete. Sufficient attention will be paid to methodological considerations, as well as gaps in the equine literature, particularly with regard to the skeletal muscle metabolome. Finally, there will be a brief discussion of the future directions and barriers to metabolomics use in the athletic horse. A thorough understanding of the metabolomics changes that occur in the equine athlete with exercise will undoubtedly help to improve horse management and health across the lifespan.

Parameter Identification through Physics-Based Impedance Models Using Impedance Spectra for Lithium-Ion Batteries

Electrochemical impedance spectroscopy (EIS) is a useful characterization tool due to its ability to separate physicochemical processes by their intrinsic time constants. EIS is extensively used to study diffusion1 and kinetic2 processes, investigate passive layer growth3, and characterize aging4 in lithium-ion batteries. However, experimental EIS data is traditionally fit using equivalent circuit models with circuit elements that are representative of the physical processes happening inside a battery. Such models may lose physical meaning and interpretability as processes and parameters are lumped together.Physics-based impedance models mathematically describe physicochemical processes based on fundamentally derived equations and are thus better alternatives for providing direct insight than their equivalent circuit analogs5. These models are often highly coupled and complex, and traditional solution methods make it difficult to solve them in a reasonable time frame suited for multi-parameter identification. Recent work on a hybrid analytical-collocation approach demonstrated significant reduction in computation time (two orders of magnitude improvement compared to commercial software), making multi-parameter estimation a more feasible optimization problem6.Estimation of parameters is an important step to using physics-based models in battery management systems (BMS) to monitor, predict, and control batteries, since such physics-based models inherently require knowledge of specific battery parameters. This work builds on the previously developed fast simulation approach for physics-based impedance models for a full cell consisting of two porous electrodes, by conducting a parameter reduction study through clustering of physical parameters. We present a method to demonstrate the identification of parameters and explore the ability to estimate parameters for the impedance spectra at different states of charge of a cell and at various states of aging.

Online Multiparameter Identification Method for Sensorless Control of SPMSM

The performance of the model-based sensorless technique depends on the accuracy of model parameters. Parameter errors between the model and the machine may result in the estimation error of the rotor position, and then degrade sensorless control performance. This article proposes an online multiparameter identification method, which is able to estimate the resistance error, inductance error, permanent magnet flux linkage, and the equivalent amplitude of the voltage distortion caused by inverter nonlinearity. Data required by the proposed identification method are obtained by injecting current pulses into the estimated d-axis. Estimating machine parameters without the help of position sensors requires solving a multivariate nonlinear regression problem, which is different from parameter identification when position sensors provide a rotor position. This article analyzes the rank-deficient problem of the proposed parameter identification method and introduces conditions that the currents in the estimated dq-axis need to meet when the rotor speed is constant. The effectiveness of the proposed parameter identification method is verified by simulation and experiment.

Voltage Vector Directional Control for IPMSM Based on MTPA Strategy

Space vector pulse width modulation (SVPWM) is used in motor control because of its high dynamic response and flexible control. Maximum torque per ampere (MTPA) strategy has become the most commonly used choice for internal permanent magnet synchronous motor (IPMSM). However, complicated coordinate transformations are required in the usual SVPWM algorithm and the performance of the MTPA algorithm can be affected by the parameters variation. This paper proposes a voltage vector directional control (VVDC) method based on the MTPA strategy. The inverse Park transformation is avoided in the proposed control scheme. And the proposed algorithm not only is briefer and clearer in the sector judgment process but also has lesser calculation during the duty cycle generation. In order to realize the MTPA strategy, two voltage feedback closed loops are designed to compensate for the d -axis and q -axis currents. A simple and effective multi-parameter identification method is also presented to solve the parameters variation problem. The effectiveness of the proposed method is verified by simulation and experiments.