Parameter Identification Method Research Articles

Adaptive PI Controller for Speed Control of Electric Drives Based on Model Reference Adaptive Identification

In this paper, to achieve auto-setting of PI controller gains when mechanical parameters are unknown, two adaptive PI controllers for speed control of electric drives are developed based on model reference adaptive identification. The adaptive linear neuron (ADALINE) neural network is used to interpret the proposed adaptive PI controller. The effect of the low-pass filter used for the feedback speed and the Coulomb friction torque on parameter identification is analysed, and a new motion equation using filtered speed is given. Additionally, a parameter identification method based on unipolar speed reference is provided. The two proposed adaptive PI controllers and the conventional PI controller are compared based on the high-precision digital simulation using MATLAB/Simulink (version R2023a). The simulation results show that both of the two proposed adaptive PI controllers are able to identify mechanical parameters, but the adaptive PI-1 controller outperforms the adaptive PI-2 controller due to its better noise attenuation performance.

A novel method for reference parameters identification and electrical property estimation of PV modules under varying operating conditions

The parameters identification of the diode model under reference condition are crucial and have significant impact on output characteristics estimation of photovoltaic (PV) module under varying operating conditions. The traditional methods of extracting model parameters have always focused on solving the parameter optimization problem only under some certain condition, which neglects their effect of performance estimation under other operating condition. In this paper, a novel method is proposed to identify the reference physical parameters and enhance the accuracy of electrical property estimation for PV modules under varying operating conditions. New objective function with penalty function is constructed by considering the estimated performance not only under reference condition but also under multiple real operating conditions. The maximum permissible error (MPE) is proposed to constrain the accuracy for key electrical output indicators and added in penalty function to ensure the accuracy under reference condition. Taking into account the inequality constraints, the guaranteed convergence particle swarm optimization with penalty function (P-GCPSO) is introduced for parameter identification. A set of comparisons between the measured and calculated results of PV modules indicate that the proposed method substantially more accurate than other documented methods for both single and double diode model. For the six different materials tested, the root mean square error (RMSE) was compared. The RMSE of the single-diode model is reduced by at least 57.98% compared to the compared method, with a maximum reduction of 86.57%. Similarly, the RMSE of the double -diode model was reduced by at least 47.24% with a maximum reduction of 88.71% compared to the compared methods. Additionally, the tests with six different types of PV modules at varying environmental conditions for over one year prove that the proposed method is reliable and practical for real-world applications. The relative error of power generation reached a minimum of 2.20%. This proves the reliability and practicability of the proposed method in practical applications. The proposed method is envisaged to be valuable for both offline analysis and online monitoring applications where an accurate, fast and consistent performance estimation tool is required.

A bootstrap-based stochastic subspace method for modal parameter identification and uncertainty quantification of high-rise buildings

This paper proposes a bootstrap-based stochastic subspace method for modal parameter identification and uncertainty quantification of high-rise buildings. Firstly, the stochastic subspace method in combination with the bootstrap technique enables the estimation of multiple sets of modal parameters from raw data series. Then, a bootstrap-based stabilization diagram is used to extract the physical modes. Finally, the modal identification and associated uncertainty quantification results are determined via statistical analysis. Through a numerical study of high-rise buildings, the performance of the proposed method is validated, demonstrating that it can provide reliable modal parameter identification and uncertainty quantification as well as has good noise immunity. Furthermore, the developed approach is employed to identify modal parameters of a 600-m-tall skyscraper during a typhoon, proving its applicability to field measurements and structural health monitoring of high-rise buildings. This paper aims to present a novel tool for modal parameter identification and associated uncertainty quantification of high-rise buildings.

Joint estimation of state-of-charge and state-of-power for hybrid supercapacitors using fractional-order adaptive unscented Kalman filter

As a new type of energy storage device, hybrid supercapacitors have the advantages of both lithium-ion batteries and supercapacitors. State of charge and state of power estimation are crucial for system operation and energy management. This work proposes a joint estimation method for the state-of-charge (SoC) and state-of-power (SoP) of hybrid supercapacitors based on a fractional-order model and unscented Kalman filter algorithm. Firstly, a parameter identification method for second-order fractional-order models is proposed using a competitive learning-based particle swarm optimization algorithm. On this basis, a SoC estimation method is designed based on the fractional-order adaptive unscented Kalman filter. Then, a SoP estimation method considering multiple constraint conditions is proposed. Finally, the proposed parameter identification and state estimation algorithms are validated under different operating dynamic conditions and environmental temperatures. The experimental results show that the error of model voltage is lower than 100 mV and the SoC estimation error is lower than 2% in the vast majority of cases, which proves the proposed algorithms have good accuracy and robustness in different environments.

Inverse Rheological Hysteresis Model and its Efficient Parameter Identification Method

Inverse Rheological Hysteresis Model and its Efficient Parameter Identification Method

МОДЕЛЮВАННЯ ХАРАКТЕРИСТИК ПРОЦЕСІВ У БІОГАЗОВИХ УСТАНОВКАХ НА ОСНОВІ АНАЛІЗУ ІНТЕРВАЛЬНИХ ДАНИХ

The problems of the research presented in the work relate to mathematical modeling in order to reflect the relationship between the main characteristic of the process and the factors that affect it, as well as the dynamics of the main characteristic of the process, which is determined by the acidity of the substrate in the bioreactor. To build mathematical models of both types, it is proposed to use the methods of parametric and structural identification of models of static objects and discrete models of object dynamics based on the analysis of interval data. An universal approach based on metaheuristic optimization algorithms is proposed and substantiated for the identification of both types of models. These methods, in turn, use mechanisms of self-organization and self-adaptation in the process of finding an optimal or quasi-optimal solution. In particular, the work uses computational algorithms built on the basis of artificial bee colony algorithms. The method is implemented using data presented in interval form. The proposed universal method was tested on the construction of a mathematical model that reflects the dependence between the pH of the fermentation medium and the volume of the loaded bio-raw material in the form of its dry and liquid parts, the temperature in the bioreactor and the humidity of the dry part of the bio-raw material. Another mathematical model built in the work reflects the dynamics of the acidity indicator pH of the fermentation medium depending on the ratio of the mass of the loaded dry bio-raw material to the volume of the loaded liquid bio-raw material Both obtained interval mathematical models can be applied to control processes in biogas plants.

Environmental Prediction Model of Solar Greenhouse Based on Improved Harris Hawks Optimization-CatBoost

Solar greenhouses provide a favorable climate environment for the production of counter-seasonal crops in northern China. The greenhouse environment is a key factor affecting crop growth, so accurate prediction of greenhouse environment changes helps to precisely regulate the crop growth environment and helps to promote the growth of fruits and vegetables. In this study, an environmental prediction model based on the combination of a gradient boosting tree and the Harris hawk optimization algorithm (IHHO-Catboost) is constructed, and in response to the problems of the HHO algorithm, such as the fact that the adjustment of the search process is not flexible enough, it cannot be targeted to carry out a stage search, and sometimes it will fall into the local optimum to make the algorithm’s search accuracy relatively poor, an algorithm based on the improved Harris hawk optimization (IHHO) algorithm-based parameter identification method is constructed. The model considers the internal and external environmental and regulatory factors affecting crop growth, which include indoor temperature and humidity, light intensity, carbon dioxide concentration, soil temperature and humidity, outdoor temperature and humidity, light intensity, carbon dioxide concentration, wind direction, wind speed, and opening and closing of upper and lower air openings of the cotton quilt, and is input into a prediction model with a time series for training and testing. The experimental results show that the MAE (mean absolute error) values of temperature, relative humidity, carbon dioxide concentration, and light intensity of the model are reduced to 49.8%, 35.3%, 72.7%, and 32.1%, respectively, compared with LSTM (Long Short-Term Memory), which is a significant decrease in error. It shows that the proposed multi-parameter prediction model for solar greenhouse environments presents an effective method for accurate prediction of environmental data in solar greenhouses. The model not only improves prediction accuracy but also reduces dependence on large data volumes, reduces computational costs, and improves the transparency and interpretability of the model. Through this approach, an effective tool for greenhouse agriculture is provided to help farmers optimize the use of resources, reduce waste, and improve crop yield and quality, ultimately leading to a more efficient and environmentally friendly agricultural production system.

Research on UAV Flight Parameter Identification Method Based on Launch Force and Airspeed.

Flight parameters are crucial criteria for UAV control, playing a significant role in ensuring the safe and efficient completion of missions. Launch force and airspeed information are key parameters in the early and middle stages of flight, serving as important data for monitoring the UAV's flight status. In response to challenges such as weak launch force, low identification rates, small airspeed, and low recognition accuracy in UAVs, a method for identifying UAV flight parameters based on launch force and airspeed is proposed. From the aspect of launch force identification, a recognition method based on a low-g value accelerometer information source is proposed, utilizing a 'multi-level time window + threshold' approach. For airspeed identification, an optimization method for airspeed measurement under the Kalman filter architecture is introduced. A device for airspeed measurement based on pressure sensors is designed, and the recommended installation position is determined through simulation. Furthermore, the feasibility and robustness of the proposed launch force identification and airspeed measurement optimization methods are validated through simulation. Finally, the effectiveness of the design is verified through centrifuge and wind tunnel experiments. This research provides technical support for the identification of the launch force and airspeed measurement in UAVs.

State estimation-based parameter identification for a class of nonlinear fractional-order systems

Parametric identification is an important part of system theory since knowledge of the parameters allows the analysis and control of the system. The aim of this paper is to propose a novel robust (against measurement noise) parameter identification method for a class of nonlinear fractional-order systems. In order to solve the parametric identification we carry out this problem to a state estimation problem, we introduce a Fractional Algebraic Identifiability (FAI) property which allows to represent the system parameters as a function of the inputs and outputs of the system, this parameter identification method provides an on-line identification process (while the system is operating), we also propose a fractional-order differentiator which allows to reduce the effect of measurement noise as well as to provide the estimation of a fractional-order derivative of the system output. Moreover, we use the Mittag–Leffler boundedness to demonstrate the convergence of this method, a different approach for this stability analysis method is given in this paper. Finally, we illustrate the accuracy and robustness of our proposed method by means of the parametric identification of two nonlinear fractional-order systems: a time-varying nonlinear fractional-order system and a nonlinear fractional-order mathematical model of a simple pendulum.

The Characterization of the Plastic Instability of S235 Thin Steel Sheets by Multiple Regression and Analysis of Variance Methods

In this study, we present a new identification method of parameters characterizing the elastoplastic damage behavior of sheet metal based on analysis of variance. The analysis covered experimental and numerical simulation results using the finite element method of the sheet mild steel bulge test. The identification procedure was validated through a confrontation between numerical simulations and experimental results on several hydroforming applications, such as a free expansion and an expansion in matrix cavities. Results show consistency between experimental observations and numerical predictions of instabilities. Furthermore, the location of risk areas of instability and rupture and the damage level could be quantified. The analysis focused on a set of results combining experimental data and numerical simulations of the bulge test of steel sheets. The identified models were validated thanks to a comparison between numerical simulations and experimental results of a set of forming applications by bulge test. The results show coherence between the experimental observations and the numerical predictions of the instabilities.

Wellbore fracture recognition and fracture parameter identification method using piezoelectric ultrasonic and machine learning

Real-time monitoring of wellbore status information can effectively ensure the structural safety of the wellbore and improve the drilling efficiency. It is especially important to recognize the wellbore fractures and identify their parameters, which motivates us to propose a wellbore fracture recognition and parameter identification method using piezoelectric ultrasonic and machine learning. To realize a self-model emission detection, we innovatively utilize a single transducer to act as both an actuator and a sensor, allowing for the efficient acquisition of ultrasonic echo signals of the wellbore. For fracture recognition, we use the wavelet packet transform to extract features from the ultrasonic echo signal, while constructing a convolutional neural network model for fracture recognition. Then, we establish the relationships between the fracture width-depth parameter and the echo signal, including the peak value as well as the arrival time difference. The experimental results show that the proposed method effectively recognizes the fractures from the ultrasonic echo signal of the wellbore. At the same time, the established function truly reflects the relationship between the fracture parameters and the echo signal. Therefore, the proposed method can provide an identification function for quantitative monitoring of wellbore fracture parameters. Moreover, the functions can be used as a reference for other structural health monitoring, which has good application prospects.

System Identification and Fractional-Order Proportional–Integral–Derivative Control of a Distributed Piping System

The vibration of piping systems is one of the most important causes of accelerated equipment wear and reduced work efficiency and safety. In this study, an active vibration control method based on a fractional-order proportional–integral–derivative (PID) controller was proposed to suppress pipeline vibration and reduce pipeline damage. First, a mathematical model of the distributed piping system was established using the finite element analysis method, and the characteristics of the distributed piping system were studied effectively. Further, the time-frequency domain parameter identification method was used to realise the system identification of the cross-point vibration transfer function between the brake and sensor, and the particle swarm optimisation algorithm was utilised to further optimise the transfer function parameters to improve the system identification accuracy. Therefore, a fractional-order PID controller was designed using the D-decomposition method, and the optimal controller parameters were obtained. The experimental and numerical simulation results show that the improved system identification algorithm can significantly improve modelling accuracy. In addition, the designed fractional-order PID controller can effectively reduce the system’s overshoot, oscillation time, and adjustment time, thereby reducing the vibration response of piping systems.

Research on a creep constitutive model of compacted graphite cast iron and its parameter identification method

AbstractIn order to accurately predict the minimum creep rate under the actual working conditions of compacted graphite cast iron cylinder head, this paper conducted creep experiments in the temperature range of 723.15–823.15 K and the stress range of 100–300 MPa. The creep damage evolution and damage mechanisms under test conditions were also analyzed. Then the data fitting performances were analyzed and compared from three aspects: the optimization‐seeking algorithm, the form of the objective function and the construction of the creep constitutive model. Finally, the chosen optimization algorithm combined with the proposed objective function and creep constitutive model resulted in 100% of the predicted values within the 3 times error band and 26.67% of the predicted values within the 1.5 times error band. This improves the prediction accuracy while reducing the effect of data overfitting.

A reduced-order electrochemical battery model for wide temperature range based on Pareto multi-objective parameter identification method

Lithium-ion batteries (LIBs) are critical components of electric vehicles and energy storage systems. However, low ambient temperatures can significantly slow down the electrochemical reaction rate and increase polarization within the battery, resulting in a reduction in capacity and power. In this paper, an accurate reduced-order electrochemical model is developed targeting for a wide temperature range (−20 to 40 °C). The model considers the excess driving force of Li + (de)intercalation in the charge transfer reaction for ion-intercalation materials by adopting adjustment in the Butler-Volmer (BV) equation. Moreover, concentration-dependent solid-phase diffusion coefficients are utilized to improve the accuracy of the model in the voltage recovery session under different charge/discharge rate conditions. To address the multi-objective optimization challenge in parameter identification across a wide range of operating conditions, the Pareto optimization method is employed. The parameters of the proposed model are identified using experimental data under different discharge conditions, including 0.2C, 0.33C, 0.5C, 1C CC discharge, and the UDDS driving cycle. To further validate the model, three dynamic conditions for testing are selected, and the model agrees well with real-world data with an average RMSE of 20 mV at different temperatures and test cycles, exhibiting its capability and robustness in predicting the battery performance under various conditions and temperatures.

Identification of Distribution Network Topology and Line Parameter Based on Smart Meter Measurements

Accurate line parameters are the basis for the optimal control and safety analysis of distribution networks. The lack of real-time monitoring equipment in grids has meant that data-driven identification methods have become the main tool to estimate line parameters. However, frequent network reconfigurations increase the uncertainty of distribution network topologies, creating challenges in the data-driven identification of line parameters. In this paper, a line parameter identification method compatible with an uncertain topology is proposed, which simplifies the model complexity of the joint identification of topology and line parameters by removing the unconnected branches through noise reduction. In order to improve the solving accuracy and efficiency of the identification model, a two-stage identification method is proposed. First, the initial values of the topology and line parameters are quickly obtained using a linear power flow model. Then, the identification results are modified iteratively based on the classical power flow model to achieve a more accurate estimation of the grid topology and line parameters. Finally, a simulation analysis based on IEEE 33- and 118-bus distribution systems demonstrated that the proposed method can effectively realize the estimation of topology and line parameters, and is robust with regard to both measurement errors and grid structures.

System-Level Performance Degradation Prediction for Power Converters Based on SSA–Elman NN and Empirical Knowledge

The degradation of power converter perfor-mance is one of the most critical issues of complex system with the improvement of power capacity and density. Power converter bears severe electrical and thermal stress, resulting in an increase in the probability of failure and significant economic losses. Most research addresses performance evaluation either through reliability theory without physical understanding or through data-driven methods requiring high experimental cost. Few studies focus on predicting system-level performance degradation, which is technically difficult as many components degrade randomly. Identifying the parameters of electronic com-ponents based on sensor data has become possible with the development of neural networks and computational power. Therefore, this paper proposes a novel system-level power degradation predicting framework, which combines the advantages of neural networks in nonlinear fitting and empirical knowledge to predict the degradation of power converter. In addition, a comprehensive and improved feature parameter screening method is proposed to iden-tify the most critical feature parameters of the power con-verter systems. Furthermore, the neural network parameter identification method based on SSA-Elman NN (Sparrow Search Algorithm - Elman Neural Network) is introduced to improve prediction accuracy. Finally, the result shows that the proposed method can accurately predict the degrada-tion of the system by using a DC-DC converter as an ex-ample.

Modeling and simulation of liquid metal battery (LMB) materials development using statistical package for social sciences (S.P.S.S.)

There are a number of input parameters that are considered in relation to the stimulatory possibility of constructing a Liquid Metal Battery (LMB). This paper talks about the modeling approach possible for use in LMB research work. Equivalent Circuit Modeling (E.C.M.) is the most common method used to analyze input data or parameters. In analyzing some of the basic elements, such as electrical capacitance, electrical resistance, open circuit voltage, terminal voltage, temperature, time response, time constants, State of Charge (SOC), etc. The cell impedance could be calculated by predicting the system elements that would play key roles in the determination of the parameter identification method for the battery’s Equivalent Circuit Model. Secondly, each element in the model has a known behaviour, which mainly depends on the element type and the values of the parameters that characterize that element. In EIS software, the Graphical Model Editor could be used to build an equivalent circuit model, or the befitting model could be carefully and properly selected. Thirdly, in fitting the Equivalent Circuit Model (ECM) to the initial data or parameters, one must take note that the parameters are strongly dependent on temperature, heat, and losses. Such are: the Open Circuit Voltage (OCV), which is strongly dependent on the temperature, the loss processes depend on temperature; losses produced by the loss processes are dissipated as heat energy; the heat generated or consumed by the electrochemical reactions during normal operation has a defined temperature; and a system with a defined temperature window with safe, stable, and efficient operation is achievable. At the end of this research, the simulation of Lithium (Li) and Cadmium (Cd) was found to be in the proportion of 67:33, which is used to determine the strength of the reactivity of the metals. It can be informed in this article that Bi and Li chemical compositions of the metals are equal to 49 for Li and 51 for Bismuth, which makes the overall reactivity very high, which could be used for LMB development.

Identification of Intensive Oscillation Parameters of Power System Based on CS-CNN-GRU Network

Broadband oscillations caused by power electronic devices contain a large number of high harmonics and interharmonics, and the frequencies of these harmonic/interharmonic components are likely to be very close. In order to improve the accuracy of dense oscillation parameter identification, this paper proposes a cyclic gate unit-convolutional neural network (CNN-GRU) dense oscillation parameter identification method optimized by the cuckoo search algorithm (CS). The Cuckoo search algorithm combined with the cross-entropy function realizes the automatic acquisition of the parameters of the CNN-GRU network and improves the adaptive ability of the model. The analysis of the measured data shows that the method in this paper has high accuracy in identifying the dense oscillation parameters of dense frequency harmonics, interharmonics, and neighboring fundamental interharmonic pairs, etc., and the computational speed is also improved compared with the traditional deep learning network.

A dynamic parameter identification method for the 5-DOF hybrid robot based on sensitivity analysis

Purpose This paper aims to propose a dynamic parameter identification method based on sensitivity analysis for the 5-degree of freedom (DOF) hybrid robots, to solve the problems of too many identification parameters, complex model, difficult convergence of optimization algorithms and easy-to-fall into a locally optimal solution, and improve the efficiency and accuracy of dynamic parameter identification. Design/methodology/approach First, the dynamic parameter identification model of the 5-DOF hybrid robot was established based on the principle of virtual work. Then, the sensitivity of the parameters to be identified is analyzed by Sobol’s sensitivity method and verified by simulation. Finally, an identification strategy based on sensitivity analysis was designed, experiments were carried out on the real robot and the results were verified. Findings Compared with the traditional full-parameter identification method, the dynamic parameter identification method based on sensitivity analysis proposed in this paper converges faster when optimized using the genetic algorithm, and the identified dynamic model has higher prediction accuracy for joint drive forces and torques than the full-parameter identification models. Originality/value This work analyzes the sensitivity of the parameters to be identified in the dynamic parameter identification model for the first time. Then a parameter identification method is proposed based on the results of the sensitivity analysis, which can effectively reduce the parameters to be identified, simplify the identification model, accelerate the convergence of the optimization algorithm and improve the prediction accuracy of the identified model for the joint driving forces and torques.

Parameter identification of solar photovoltaic models by multi strategy sine–cosine algorithm

AbstractAccurate modeling and parameter identification of photovoltaic (PV) cells is a difficult task due to the nonlinear characteristics of PV cells. The goal of this paper is to propose a multi strategy sine–cosine algorithm (SCA), named enhanced sine–cosine algorithm (ESCA), to evaluate nondirectly measurable parameters of PV cells. The ESCA introduces the concept of population average position to increase the population exploration ability, and at the same time introduces the personal destination agent mutation mechanism and competitive selection mechanism into SCA to provide more search directions for ESCA while ensuring the search accuracy and diversity maintenance. To prove that the proposed ESCA is the best choice for extracting nondirectly measurable parameters of PV cells, ESCA is evaluated by the single‐diode model, the double‐diode model, the three‐diode model, and the photovoltaic module model (PVM), and compared with eight existing popular methods. Experimental results show that ESCA outperforms similar methods in terms of diversity maintenance, high efficiency, and stability. In particular, the proposed ESCA method is less than the SCA by 0.081, 0.144, and 0.578 in the standard deviation statistics metrics of the three PVM models (PV‐PWP201, STM6‐40/36, and STP6‐120/36), respectively. Therefore, the proposed ESCA is an accurate and reliable method for parameter identification of PV cells.