Parameter Identification Research Articles

Auxiliary model maximum likelihood least squares-based iterative algorithm for multivariable autoregressive output-error autoregressive moving average systems

Identification of multivariable systems is of great significance to control systems. This paper focuses on the parameter identification problems for multivariable autoregressive output-error autoregressive moving average (M-AROEARMA) systems. On the basis of the decomposition strategy, the M-AROEARMA model is de- composed into multiple subsystem models. By means of the auxiliary model idea, the auxiliary model least squares-based iterative algorithm is derived. For the purpose of achieving highly accurate parameter identification performance under colored noises interference, an auxiliary model maximum likelihood least squares-based iterative algorithm is proposed by utilizing the maximum likelihood principle. The numerical simulation example demonstrates the effectiveness of the proposed algorithms.

Security Risk Assessment of Teleoperated Vessels and Associated Centers: Parameter Identification

Security Risk Assessment of Teleoperated Vessels and Associated Centers: Parameter Identification

Understanding Multi‐horizon Forecasts: Identification, Estimation and Testing

The properties of multi‐horizon forecasts are investigated by specifying and estimating a model nesting rational forecasts, as well as deviations from rationality resulting from horizon‐specific biases and idiosyncratic errors. Identification of the model's parameters is achieved by combining information on forecasts for a given point in time with different information sets, and forecasts at different points in time based on the same information set. Both sets of forecasts are used to estimate the model's parameters by generalised method of moments. A test of rationality is proposed with the twin advantages of circumventing parameter identification and boundary issues. The finite sample properties of the GMM estimator and the rationality test are also investigated. Applying the approach to US GDP growth forecasts of the Survey of Professional Forecasters, the empirical results show that forecasts deviate from rationality over all forecast horizons, with the strength of the deviations from rationality increasing with the forecast horizon.

Determination of the Thermal Conductivity and Volumetric Heat Capacity of Substance from Heat Flux

The study of nonlinear problems related to heat transfer in a substance is of great practical important. Earlier, this paper’s authors proposed an effective algorithm for determining the volumetric heat capacity and thermal conductivity of a substance based on experimental observations of the dynamics of the temperature field in the object. In this paper, the problem of simultaneous identification of temperature-dependent volumetric heat capacity and thermal conductivity of the substance under study from the heat flux at the boundary of the domain is investigated. The consideration is based on the first boundary value problem for a one-dimensional unsteady heat equation. The coefficient inverse problem under consideration is reduced to a variational problem, which is solved by gradient methods based on the application of fast automatic differentiation. The uniqueness of the solution of the inverse problem is investigated.

Closed-loop control strategy design for Caputo–Fabrizio definition-based fractional-order Boost converters considering parametric uncertainties

This paper proposes a dual closed-loop adaptive PI control system based on parameter identification for Caputo–Fabrizio (C-F) definition-based fractional-order boost converters. First, a C-F definition-based fractional-order model of the Boost converter is derived. Afterward, an online component parameter identification method is proposed to obtain the values and orders of capacitors and inductors in the proposed model. Based on these identified parameters, a dual closed-loop adaptive PI control system considering parametric uncertainties for the Boost converters is designed using the frequency domain analysis method. Finally, simulation validations are conducted. The online parameter identification method can accurately identify the values and orders of capacitors and inductors. The identification error is less than [Formula: see text] for the capacitance value and [Formula: see text] for the other parameters. Moreover, the proposed control system can automatically adjust the parameters with changes in the component parameters, effectively improving the robustness. Compared to traditional PI controllers, when the parameters of components change, the proposed control system can reduce voltage overshoot by [Formula: see text] in the case of sudden changes in the input voltage. These results confirm the efficacy of the proposed online parameter identification method and the dual closed-loop adaptive PI control system.

Remote SOC and SOH Estimation of High Energy Lithium Batteries Based on Dual-Mode Extended Kalman Filter Algorithm

Lithium battery, as its main power source, needs to ensure the safety of drivers and passengers not only under some complex external conditions, but also under harsh use conditions, even when damaged. At some point throughout this process, it is required to evaluate the status of the battery itself in order to assure safe usage of the battery and to develop a more effective battery management plan. SOC, SOH, and condition of power are all variables that are commonly used to describe the state of a lithium battery (SOP). The ability of the battery to constantly provide or receive power, the remaining service the life cycle of the battery, and the ability of the battery to output or receive power promptly are all described by these three characteristics. In order to effectively evaluate the health status of batteries, this paper proposes a dual-mode extended Kalman filter (EKF) algorithm for the remote estimation of SOC and SOH of high-energy lithium batteries. In the estimating procedure, the open circuit voltage (OCV) is also included as a state variable in the iterative process, which allows for more accurate results. In this paper, the state space equation is established based on the first-order RC equivalent circuit model, and the battery state estimation and parameter identification are completed by using the double EKF (the dual extended Kalman filtering, DEKF) algorithm, resulting in the realization of the estimation of SOC and SOH.

Research and Evaluation of a New Structural Damage Identification Method Based on a Refined Genetic Algorithm

In order to solve the problem of structural damage location and degree identification, the weighted mean of vectors algorithm (INFO), a high-performance optimization algorithm, was first introduced to identify structural damage. By comparison with the refined genetic algorithm (RGA), the accuracy and advantages of INFO are analyzed and evaluated. An objective function is constructed by combining the dynamic response transfer ratio without modal analysis. The INFO and RGA algorithms are used to optimize the objective function for damage identification. The simulation results verify the effectiveness of the three damage identification methods. The results show that the identification effect of the INFO algorithm can reach nearly 100% without noise influence, and the anti-noise ability is the strongest. Among the three algorithms, the damage identification accuracy of the INFO algorithm is the highest, followed by the RGA algorithm and the GA algorithm.

Response reconstruction based on measurement matrix optimization in compressed sensing for structural health monitoring

Structural health monitoring (SHM) data have a large volume, increasing the cost of data storage and transmission and the difficulties of structural parameter identification. The compressed sensing (CS) theory provides a signal acquisition and analysis strategy. Signal reconstruction using limited measurements and CS has attracted significant interest. However, the dynamic responses obtained from civil engineering structures contain noise, resulting in sparse samples and reducing the signal reconstruction accuracy. Therefore, we propose an optimization algorithm for the measurement matrix integrating the Karhunen-Loeve transform (KLT) and approximate QR decomposition (KLT-QR) to improve the accuracy of dynamic response reconstruction of SHM data. The KLT reduces the correlation between the measurement matrix and the sparse basis. The approximate QR decomposition is used to improve the independence between the column vectors of the measurement matrix, optimizing the measurement matrix. The experimental results for a laboratory steel beam indicate that the proposed KLT-QR algorithm outperforms three other algorithms regarding the accuracy of dynamic response reconstruction (acceleration, displacement, and strain), especially at high compression ratios. The acceleration responses from the Ji’an Bridge are utilized to verify the advantages of the proposed algorithm. The results demonstrate that the KLT-QR algorithm has the highest accuracy of reconstructing the vibration signals and yields better Fourier spectra than the conventional Gaussian measurement matrix.

Line-parameter identification of medium-voltage distribution systems based on deep deterministic policy gradients

Accurate line-parameter identification is an important foundation for refined the regulation, protection, and control of distribution systems. Traditional identification models provide accurate modeling, while conventional identification approaches are hindered by the high complexity and low observability of power systems. In this article, a parameter identification method based on the deep deterministic policy gradient is proposed for medium voltage distribution systems. The proposed method starts with objective function constructing, followed by power flow analysis and parameter identification modeling, where the L2 normalization theory is introduced to improve the computation efficiency. On this basis, the parameter identification framework is constructed through designing the Markov decision process of a parameter and using a training mechanism. An adaptive parameter correction method is proposed to improve the accuracy and efficiency of a deep-reinforcement-learning-based agent. The performance of the proposed modal is tested on IEEE 14-node and IEEE 33-node medium-voltage distribution systems. Case simulation results demonstrate that the proposed modal exhibits superior computational capability, while achieving fewer errors compared to traditional methods.

Inverse Identification of Constituent Elastic Parameters of Ceramic Matrix Composites Based on Macro–Micro Combined Finite Element Model

Accurate material performance parameters are the prerequisite for conducting composite material structural analysis and design. However, the complex multiscale structure of ceramic matrix composites (CMCs) makes it extremely difficult to accurately obtain their mechanical performance parameters. To address this issue, a CMC micro-scale constituents (fiber bundles and matrix) elastic parameter inversion method was proposed based on the integration of macro–micro finite element models. This model was established based on the μCT scan data of a plain-woven CMC tensile specimen using the chemical vapor infiltration (CVI) process, which could reflect the real microstructure and surface morphology characteristics of the material. A BP neural network was used to predict the multiscale stiffness, considering the influence of the porous structure on the macroscopic stiffness of the material. The inversion process of the constituent elastic parameters was established using the trust-region algorithm combined with an improved error function. The inversion results showed that this method could accurately invert the CMC constituent elastic parameters with excellent robustness and anti-noise performance. Under four different degrees of deviation in the initial iteration conditions, the inversion error of all parameters was within 1%, and the maximum inversion error was only 2.16% under a 10% high noise level.

Robust Estimation of Lithium Battery State of Charge with Random Missing Current Measurement Data

The precise estimation of the state of charge (SOC) in lithium batteries is crucial for enhancing their operational lifespan. To address the issue of reduced accuracy in SOC estimation caused by the random missing values of lithium battery current measurements, a joint estimation method which combines recursive least squares with missing input data (MIDRLS) and the unscented Kalman filter (UKF) algorithm is proposed, called the MIDRLS-UKF algorithm. Firstly, the equivalent circuit model of a Thevenin battery is formulated. Then, the current imputation model is designed to interpolate the missing data, based on which the MIDRLS algorithm is derived by solving the unbiased estimation of the gradient of the objective function, thus realizing the online high-precision identification of the circuit model parameters. Furthermore, the proposed algorithm is combined with the UKF algorithm to facilitate the online precise estimation of SOC. The simulation results indicate a marked decrease in the SOC estimation error when employing the proposed joint algorithm, as opposed to the conventional forgetting factor recursive least squares (FFRLS) algorithm combined with the UKF joint estimation algorithm, which verifies the precision and effectiveness of the proposed joint algorithm.

Intelligent-based approach for parameters identification and control of fractional order systems

The optimization algorithms for identification and control processes take more and more place in the industrial domain. However, determining a control law for fractional systems remains challenging for researchers today. This paper presents a simple method for using a fractional PID (FOPID) corrector to regulate a class of fractional systems that show a stable step response. The procedure is based on an algorithmic identification to assimilate the fractional system into a simple model. Optimization methods were used. Using techniques like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO) to ascertain the parameters of the basic model, a first-order system with delay. Then, the FOPID controller will be calculated using Ziegler–Nichols methods and optimization algorithms to stabilize the real process. Better performance Overshoot and Settling time as well as minimal Energy control effort are benefits of the proposed design. To confirm that the fractional regulator is robust which is optimized by the best-chosen method (PSO), the parameters of the chosen process will be randomly varied. Through two numerical simulations, the efficiency of the suggested method has been confirmed.

Modeling Thermal Impedance of IGBT Devices Based on Fractional Calculus Techniques

The thermal impedance characteristics of insulated gate bipolar transistor (IGBT) modules are critical for the thermal management and design of electronic devices. This paper proposes a fractional-order equivalent thermal impedance model, which is inspired by the correlation between multi-time-scale dissipation characteristics of heat conduction processes and fractional calculus. The fractional-order equivalent thermal impedance model is derived based on the connection between fractional-order calculus and the Foster thermal network model in mathematical operations, with only two parameters to be identified: heat capacity C and fractional order α. Moreover, this paper provides a parameter identification method for the proposed fractional-order equivalent thermal impedance model based on the multi-objective particle swarm optimization (MOPSO) algorithm. In order to validate the effectiveness and superiority of this work, experiments and comparative works are provided in this paper. The results indicate that the fractional-order equivalent thermal impedance model can accurately describe the frequency domain characteristic curves of the thermal impedance of the Foster thermal network model for IGBT modules, with the difference between the amplitude frequency characteristics not exceeding 1 dB and the difference between the phase frequency characteristics not exceeding 1° within the operating frequency range of (1 kHz, 1 MHz).

Parameter Identification of Solid Oxide Fuel Cell Using Elman Neural Network and Dynamic Fitness Distance Balance-Manta Ray Foraging Optimization Algorithm

An accurate solid oxide fuel cell model is a prerequisite for optimizing the operation and state estimation of subsequent cell systems. Hence, this work aimed to utilize a vigoroso algorithmic tool, i.e., Elman neural network, for data prediction to enrich cell measurement data and employ the trained network model for noise reduction of voltage–current data. Furthermore, to obtain reliable cell parameters, a novel parameter identification model based on the dynamic fitness distance balance-manta ray foraging optimization (dFDB-MRFO) algorithm is proposed. Two datasets were applied to extract the electrochemical model and simple electrochemical model parameters of the solid oxide fuel cell model. To verify adequately the superiority of this method, which is compared with another seven conventional heuristic algorithms, four performance indicators were selected as evaluation criteria. Comprehensive case studies demonstrated that through data processing, the precision and robustness of identification could be effectively heightened. In general, the model fitting data obtained via parameter identification using dFDB-MRFO have excellent fitting precision contrast with the measured voltage–current data. Notably, the fitting degree obtained by dFDB-MRFO in the simple electrochemical model reached 99.95% and 99.91% under the two datasets, respectively.

SIMULATION OF PREHEATING AND HEATING WHEN CONNECTING POLYETHYLENE PIPES WITH ELECTROFUSION COUPLINGS

Existing machines for electrofusion welding of polyethylene pipes provide a high-quality connection at air temperatures no lower than minus 10 °C. However, if welding is required at lower temperatures, a layer of heat-insulating material is used to reduce the cooling rate. This insulation is selected based on the air temperature and the size of the welded pipes. This paper investigates the dynamics of temperature fields at the stages of heating, temperature equalization and heating using a new technology of welding at low temperatures. The proposed technology eliminates the use of thermal insulation material and allows welding to be carried out automatically.The paper presents a mathematical model that describes the thermal process when performing all welding operations at low temperatures. The model describes the thermal process with and without phase transformation. The well-known method of end-to-end calculation with the introduction of effective heat capacity is used to consider the heat of phase transition in the temperature range. The model also considers the dependence of the thermophysical properties of polyethylene on the degree of crystallinity.The following text presents the results of preheating and temperature equalization calculations for welding polyethylene pipes of the PE 100 SDR 11 brand with a diameter of 160 mm at an air temperature of minus 40 °C. To accurately describe the thermal process of real welding, experimental temperature data was used for parametric identification. It is shown that as a result of preheating and temperature equalization in the weld and thermally affected zones and outside them at a distance of more than 10 mm, the temperature values do not exceed acceptable limits. The resulting temperature distribution allows heating according to the standard welding mode. Calculations show that when the heating is completed, the volume of the polyethylene melt corresponds to the volume of the welding melt at the allowed air temperature.These findings can be used to develop a technique for controlling the movement of the crystallization front during welding at low temperatures.

Combined Identification of Vehicle Parameters and Road Surface Roughness Using Vehicle Responses

Highways, urban roads, and bridges are the important transportation infrastructures for the economic development of modern society. The evaluation of bridge and road quality is crucial to the maintenance and management of the bridge and road industry. Road roughness is a widely accepted indicator in the evaluation of road quality and safety, which is a major input source for vehicles. The vehicle responses-based method of identifying road roughness is efficient and convenient. However, the dynamic characteristics of the vehicle have an important impact on the interaction between the vehicle and the road. When the vehicle parameters are not yet clear, the coupling of unknown parameters and unknown road roughness results in the need for mutual iteration when the existing methods simultaneously identify vehicle parameters and road roughness. To address this issue, this study proposes an effective method for the combined identification of vehicle parameters and road roughness using vehicle responses. The test vehicle is modeled as a four-degree-of-freedom half-vehicle model. In view of the coupling effect between tire stiffness and road roughness, the unknown vehicle physical parameters, except for tire stiffness, are first included in the extended state vector. Based on the extended Kalman filter for unknown excitation (EKF-UI), unknown vehicle physical parameters and unknown forces on the axle are identified. Subsequently, based on the property that the front and rear axles of the vehicle pass through the same road roughness area at a fixed time lag, the tire stiffness is identified by combining the identified unknown forces on the axle. Finally, the road roughness is obtained using the identified vehicle parameters and unknown forces. Numerical studies with different levels of roughness, different noise levels, and different vehicle speeds have verified the accuracy of this method in identifying vehicle parameters and road roughness.

Divergence and Similarity Characteristics for Two Fuzzy Measures Based on Associated Probabilities

Abstract: The article deals with the definitions of the distance, divergence, and similarity characteristics between two finite fuzzy measures, which are generalizations of the same definitions between two finite probability distributions. As is known, a fuzzy measure can be uniquely represented by the so-called its associated probability class (APC). The idea of generalization is that new definitions of distance, divergence, and similarity between fuzzy measures are reduced to the definitions of distance, divergence, and similarity between the APCs of fuzzy measures. These definitions are based on the concept of distance generator. The proof of the correctness of generalizations is provided. Constructed distance, similarity, and divergence relations can be used in such applied problems as: determining the difference between Dempster-Shafer belief structures; Constructions of collaborative filtering similarity relations; non-additive and interactive parameters of machine learning in phase space metrics definition, object clustering, classification and other tasks. In this work, a new concept is used in the fuzzy measure identification problem for a certain multi-attribute decision-making (MADM) environment. For this, a conditional optimization problem with one objective function representing the distance, divergence or similarity index is formulated. Numerical examples are discussed and a comparative analysis of the obtained results is presented.

A novel friction coefficient model parameters identification method for needle-tissue insertion procedure

Abstract In endoscopic liver vascular insertion surgeries, during the process of angiographic operation, the success of vascular staining depends on precise needle insertion control which heavily relies on experienced surgeons. Endoscopic vascular insertion surgical navigation system shows the potential to improve position precision, however it relies on needle-tissue interaction model and parameter identification to provide essential information for improving needle insertion accuracy, in which the friction coefficient is important parameters but difficult to determine. In this paper, a novel needle-tissue friction coefficient identification method was proposed with unknown tissue Young's modulus under endoscopic liver surgery scenarios. A modified friction coefficient model was proposed including the adhesion and elastic friction component to describe needle-tissue dynamic interaction process which can predict the friction coefficient more precisely. The proposed parameter estimation method based on the modified friction model can simultaneously estimate friction coefficient and Young's modulus. The proposed method was demonstrated by the friction coefficient measurement experiment. The results showed that the friction coefficient model prediction results agreed well with expected value. The proposed method can be applied to provide essential tissue-needle interactive information to improve needle insertion precision in the endoscopic liver vascular insertion surgery scenarios.

Rapid Optimization of Active Disturbance Rejection Controller Parameters for Quadrotor UAVs Using Kriging Surrogate Modeling

The Active Disturbance Rejection Controller (ADRC), celebrated for its superior resistance to interference, presents itself as an exemplary solution for the development of control systems that are designed to accommodate substantial changes in payload weight for quadrotor Unmanned Aerial Vehicles (UAVs) and to endure robust side winds along with other challenging operational scenarios. Despite the inherent complexity due to the numerous parameters required for the configuration of the ADRC, an innovative method utilizing the Kriging surrogate optimization algorithm has been introduced to automate and expedite the generation of these controller parameters. The development of the ADRC begins with the dynamics model of the quadrotor UAV, followed by the identification of key design parameters. These parameters are then rapidly optimized through the Kriging surrogate optimization algorithm. The controller’s effectiveness is confirmed by implementing the ADRC on Pixhawk flight control hardware, with a comparative analysis of the attitude response under various operating conditions, thereby validating the ADRC’s superior anti-disturbance performance.

Identification of fractional-order transfer functionsand nonzero initial conditions using exponentiallymodulated signals

Abstract A new methodology that uses exponentially modulated signals with arbitrary excitation waveforms for the identification of fractional order transfer functions is proposed. In contrast to previous approaches where initial conditions were not considered and the system was required to be at rest for the identification procedure, the current contribution extends the formulation to the case where the system has non-zero initial conditions, dispensing with the need to place it at a resting state. This generalization is important in feedback instrumentation and metrology applications where the measurement or control process may not be disrupted to perform identification. Moreover, the procedure has a broader scope of applications because it structurally contemplates the case when the model presents derivatives in the input. Full identification of the system parameters as well as the fractional exponents associated with the model dynamics are achieved through a grid search procedure with resolution adjustable by the user. Two simulation examples are presented to illustrate the effectiveness of the proposed approach. The first example is concerned with the effect of measurement noise at the observed system output, whereas the second involves the identification of the impedance of a three-dimensional RC network model. These types of RC networks
have dynamics capturing complex phenomena with emergent responses and are ideal for emulating the complex dynamics encountered across physical sciences and in particular interdisciplinary subject areas such as biomedical engineering.