Identification Of Model Parameters Research Articles

Nullspace Adaptive Identification of Plant and Actuator Model Parameters for Underactuated Ground Vehicles: Theory and Experimental Evaluation

Nullspace Adaptive Identification of Plant and Actuator Model Parameters for Underactuated Ground Vehicles: Theory and Experimental Evaluation

Subject-Specific Parametric Identification of a Spine-Equivalent Beam Model for Lumbar Force Prediction in Sagittal Plane

Subject-Specific Parametric Identification of a Spine-Equivalent Beam Model for Lumbar Force Prediction in Sagittal Plane

Extended Linear Regression and Interior Point Optimization for Identification of Model Parameters of Fixed Wing UAVs

Extended Linear Regression and Interior Point Optimization for Identification of Model Parameters of Fixed Wing UAVs

Modeling Patient-specific Apnea-bradycardia Patterns in Preterm Newborn.

Preterm infants are particularly exposed to severe cardio-respiratory events, associating apnea with bradycardia and oxygen desaturation. A patient-specific and event-specific model-based approach is proposed in this work to analyze the acute heart rate response to apnea-bradycardia events in preterm newborn. A novel model integrating the main cardio-respiratory interactions which are specific to the neonatal period is proposed. An evolutionary algorithm is applied to estimate patient-specific model parameters from a database of 37 apnea-bradycardia episodes acquired from 10 preterm newborns. Unsupervised clustering (K-means) was applied to the identified parameters to define phenogroups of cardio-respiratory responses to apnea. A significant correspondence was observed between simulated and experimental heart rate series in all identifications (median RMSE = 8.85 bpm). Three clusters of parameters were found and were associated to three different pathophysiological dynamics related to apnea-bradycardia. The proposed method, based on patient and event-specific model parameter identification, provides a novel approach to characterize bradycardia dynamics in response to apnea, opening the way to the proposal of new personalized diagnosis and treatment possibilities in this particularly sensitive population.

Analysis of Electrical Aging Effects on AC High Frequency Motor Based on Exchange Market Algorithm Model Parameter Identification

Analysis of Electrical Aging Effects on AC High Frequency Motor Based on Exchange Market Algorithm Model Parameter Identification

An Improved Multi-time Scale Lithium-ion Battery Model Parameter Identification Algorithm Based on Discrete Wavelet Transform Method

An Improved Multi-time Scale Lithium-ion Battery Model Parameter Identification Algorithm Based on Discrete Wavelet Transform Method

Application research of interior space design elements based on image processing

In order to quantitatively analyze the application performance of elements in interior space design, a detection method of texture visual features of elements distribution images in interior space design based on image processing and texture edge contour features detection is proposed. A multi-resolution visual information collection model for detecting the texture visual characteristics of element distribution images in indoor space design is constructed, and a visual characteristic identification model for element application configuration in indoor space design is established. Multi-texture grid block area combination design method is adopted to realize the fusion of element application configuration visual images in indoor space design. Through the edge feature detection method of texture grid area, Establish a visual parameter identification model of element application configuration in interior space design, extract the visual information feature quantity of element application configuration in interior space design, adopt Harris corner feature matching method, realize distributed detection of texture edge contour features of element application configuration visual features in interior space design, and fuse detection of high-resolution texture information through combined filtering and collaborative filtering. The collection and optimization of visual features of element application configuration in interior space design are realized, and the distributed detection algorithm of texture edge contour features is adopted to improve the convergence level of detection of texture visual features of element distribution images in interior space design, and the application of elements in interior space design based on image processing is realized. The simulation results show that this method has good resolution and strong visual expression ability in extracting the texture visual features of element distribution images in interior space design, which improves the quantitative analysis ability of elements in interior space design.

Additive singular spectral model of a dendrochronological signal

Relevance. Allocation of structural components in a dendrochronological signal of annual rings of coniferous trees expands the possibilities of the bioindication method and allows obtaining information about changes in environmental conditions in the past for extended territories. Aim. Creation of an additive singular spectral model based on the frequency trigonometric components of the dendrochronological signal; reconstruction of changes in the total ozone content in the atmosphere in the past, affecting the level of ultraviolet radiation in the B range radiation. Objects. Time series of total ozone content in the atmosphere (data from 1932), width and density of annual rings (data from 1686–2004) on the example of the territory near the observatory in Arosa, Switzerland, time series of stable oxygen isotopes δ18Oc, France. Methods. Time series analysis (decomposition of a time series, identification of model parameters, prediction of a time series), statistical analysis (F-criterion, χ2 – Pearson criterion), experimental measurements of the percentage components of the wood of annual rings, correlation analysis, spectral singular analysis. Results. Decomposition of the dendrochronological signal of individual chronologies into trigonometric components in the Caterpillar program. Correlation analysis of the sensitivity of trees to atmospheric effects. Reconstruction of the ozone level in the stratosphere using the first trigonometric component of the dendrochronological signal. In cellulose, the first low-frequency component of the signal and the structural component of wood, a reliable response of trees to long-period fluctuations in the total ozone content is recorded. This allows assessment of the impact of the stress factor on conditions of exploitation of forest resources. The use of data on the width of the annual rings allows you to expand the territorial boundaries of the method.

A fractional-order modelling and parameter identification method via improved driving training-based optimization for piezoelectric nonlinear system

The accurate identification of the parameters of fractional-order systems is still a challenging problem. The purpose of this paper is to establish a fractional hysteresis model based on a Bouc-Wen differential equation and an improved driving training-based optimization (DTBO) parameter identification method to accurately characterize the nonlinear characteristics of the piezoelectric hysteresis system. In this paper, there are two contributions. On the one hand, an accurate fractional-order hysteresis model is proposed and its rate-dependent property is proved based on the definition of fractional calculus; on the other hand, an improved DTBO optimization method with enhanced performance is proposed to achieve excellent precision and robustness for the purpose of parameter identification. The unique features of the improved DTBO algorithm include an instructor-learner separation strategy, a new update mechanism of instructor position, and an improved phase of the learner group. The results show that the output of the model is consistent with that of the actual piezoelectric platform. Compared with the standard DTBO algorithm and Antlion Optimization algorithm, the improved DTBO algorithm has better performance. The novelty of this paper is that a new high-performance heuristic optimization method is proposed for the particular problem of the parameter identification of fractional-order hysteresis models. The proposed method holds promise as a good candidate in the field of hysteresis modelling and identification.

Battery parameter identification method of a battery module based on a multi-physical measurement system

The secondary utilization of retired electric vehicle batteries is beneficial for improving resource utilization efficiency. Capacity and internal resistance are battery parameters that can reflect the battery state. To identify the parameters of a single battery in a battery module, it is usually necessary to disassemble the battery module. The process is complex, time-consuming, and unsafe. In this paper, a battery parameter identification method without disassembling the battery module is developed based on a multi-physical measurement system. First, a multi-physical electrical-thermal-spatial measurement system is constructed to measure the physical parameters of the battery module during charging and discharging. Then, the electrical model and the thermal model are developed. To obtain the capacity and internal resistance of each cell within the battery module, a battery parameter identification model is established with temperature and total battery current as input parameters and battery capacity and internal resistance as output parameters. Batteries with different capacities and internal resistances are connected in series and parallel to simulate retired battery modules for electric vehicles. The battery parameters are identified using the method presented in this paper. According to the experiments, the relative errors of parameter identification for battery capacity and internal resistance are both within 9 %. This method can improve the efficiency of the battery screening process as well as the reliability of the battery system.

Aging-Sensitive Fast-Charging for Heavy-Duty Electric Vehicles

To use lithium-ion cells on-board electric vehicles (EVs) safely and efficiently, a battery management system (BMS) is essential. It monitors the battery’s states and controls utilization by limiting e.g., current and temperature. Advanced BMS increasingly use electrochemical models to assess internal states of battery cells and apply usage constraints based on these [1]. The performance of an electrochemical BMS is closely tied to the performance of the underlying model and the accuracy of its parameters [2]. We employ a novel re-parametrization method to update electrochemically constrained optimal fast-charging procedure to minimize battery degradation. The parameters of electrochemical models are typically identified through a combination of physico-chemical characterization techniques and curve fitting [3]. Since model parameters change with aging [4], there is a need to re-parametrize electrochemical models on-board to conserve model accuracy. As basis for this re-parametrization, we propose a novel reference performance test that can be incorporated into a conventional over-night charge and performed intermittently.Optimal control has previously been demonstrated for fast-charging in EV applications [1]. However, electrochemical, and mechanical degradation need to be considered to allow optimal fast charging throughout batteries’ lifetimes. Khalik et al. [5] demonstrated how a charging procedure could be designed a-priori for the entire cycle life, considering a direct implementation of the solid-electrolyte-interphase growth and capacity fade due to the associated lithium consumption. Such approaches can extend battery lifetime, but neither yield optimal charging-procedures at every state-of-health nor handle unknown aging modes on the fly. Our proposed approach involves intermittent identification of electrochemical model parameters and subsequent update of the fast-charging protocol such that it adheres to electrochemical constraints throughout the entire battery lifetime.In this work, summarized in Figure 1, we present this methodology and show results from an experimental study testing the proposed approach vs. conventional charging procedures. The experimental study is performed on state-of-the art automotive cells used in heavy-duty electric vehicles.Figure caption: Comparison of a conventional electrochemically constrained fast-charging procedure with the aging-sensitive approach proposed in the present work.

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Cultivation process (CP) modeling and optimization are ambitious tasks due to the nonlinear nature of the models and interdependent parameters. The identification procedures for such models are challenging. Metaheuristic algorithms exhibit promising performance for such complex problems since a near-optimal solution can be found in an acceptable time. The present research explores a new hybrid metaheuristic algorithm built upon the good exploration of the genetic algorithm (GA) and the exploitation of the crow search algorithm (CSA). The efficiency of the proposed GA-CSA hybrid is studied with the model parameter identification procedure of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation process. The results are compared with those of the pure GA and pure CSA applied to the same problem. A comparison with two deterministic algorithms, i.e., sequential quadratic programming (SQP) and the Quasi-Newton (Q-N) method, is also provided. A more accurate model is obtained by the GA-CSA hybrid with fewer computational resources. Although SQP and Q-N find a solution for a smaller number of function evaluations, the resulting models are not as accurate as the models generated by the three metaheuristic algorithms. The InterCriteria analysis, a mathematical approach to revealing certain relations between given criteria, and a series of statistical tests are employed to prove that there is a statistically significant difference between the results of the three stochastic algorithms. The obtained mathematical models are then successfully verified with a different set of experimental data, in which, again, the closest one is the GA-CSA model. The GA-CSA hybrid proposed in this paper is proven to be successful in the collaborative hybridization of GA and CSA with outstanding performance.

SEAM: An integrated activation-coupled model of sentence processing and eye movements in reading

Models of eye-movement control during reading, developed largely within psychology, usually focus on visual, attentional, lexical, and motor processes but neglect post-lexical language processing; by contrast, models of sentence comprehension processes, developed largely within psycholinguistics, generally focus only on post-lexical language processes. We present a model that combines these two research threads, by integrating eye-movement control and sentence processing. Developing such an integrated model is extremely challenging and computationally demanding, but such an integration is an important step toward complete mathematical models of natural language comprehension in reading. We combine the SWIFT model of eye-movement control (Seelig et al., 2023) with key components of the Lewis and Vasishth sentence processing model (Lewis and Vasishth, 2005). This integration becomes possible, for the first time, due in part to recent advances in successful parameter identification in dynamical models, which allows us to investigate profile log-likelihoods for individual model parameters. We present a fully implemented proof-of-concept model demonstrating how such an integrated model can be achieved; our approach includes Bayesian model inference with Markov Chain Monte Carlo (MCMC) sampling as a key computational tool. The integrated Sentence-Processing and Eye-Movement Activation-Coupled Model (SEAM) can successfully reproduce eye movement patterns that arise due to similarity-based interference in reading. To our knowledge, this is the first-ever integration of a complete process model of eye-movement control with linguistic dependency completion processes in sentence comprehension. In future work, this proof of concept model will need to be evaluated using a comprehensive set of benchmark data.

Ideal Agent System with Triplet States: Model Parameter Identification of Agent-Field Interaction.

On the capital market, price movements of stock corporations can be observed independent of overall market developments as a result of company-specific news, which suggests the occurrence of a sudden risk event. In recent years, numerous concepts from statistical physics have been transferred to econometrics to model these effects and other issues, e.g., in socioeconomics. Like other studies, we extend the approaches based on the "buy" and "sell" positions of agents (investors' stance) with a third "hold" position. We develop the corresponding theory within the framework of the microcanonical and canonical ensembles for an ideal agent system and apply it to a capital market example. We thereby design a procedure to estimate the required model parameters from time series on the capital market. The aim is the appropriate modeling and the one-step-ahead assessment of the effect of a sudden risk event. From a one-step-ahead performance comparison with selected benchmark approaches, we infer that the model is well-specified and the model parameters are well determined.

Identification and modelling of parameters for the information-physical-social convergence characteristics of user-side flexible resources

The use of flexible resource information on the user side helps to increase system efficiency. Power system power variation becomes more pronounced with the access to renewable resources. Therefore, the study proposes a parameter identification and modeling method for the physical and social integration characteristics of flexible resource information on the user side. Taking the user’s air conditioning load as the object, the thermal dynamic model of the air conditioning building is constructed using equivalent thermal parameters, and the variable frequency air conditioning load is embedded in the battery model. The model parameter identification is carried out using high-dimensional model expression technology. According to the experimental data, in options 2 and 3, the system operator makes power purchases based on the storage status of the lithium battery or virtual battery, increasing the number of power purchases when the price of electricity is low and decreasing the number of power purchases when the price of electricity is high. This effectively reduces the system operator’s electricity costs. The error of multiple linear regression modelling varies widely, with relative errors up to 0.75 and an average relative error of 15.1%. The relative error of modelling based on the high-dimensional model expression technique is in the range of 0 to 0.2, with an average relative error of 5.5%. The results show that compared with multiple linear regression models, high-dimensional model representation technology has higher modeling accuracy and can accurately identify the parameters of the air conditioning load aggregation model, solving the problem of difficult parameter calculation in the practical application of the air conditioning load aggregation model, and providing technical support for power system regulation.

Examination of machine learning method for identification of material model parameters

In this work, we compare two methods of using artificial neural network (ANN) to determine the optimal parameters of material model by results of high-speed plate impact experiments. The first ANN is trained to determine the free surface velocity history based on the impact parameters and material model parameters; this ANN is used as a fast emulator of model to speed-up the statistical Bayesian calibration of the model parameters. The second ANN directly determines the material model parameters based on the impact parameters and the free surface velocity history, because this ANN is trained by inverse data set; this approach for inverse problem is proposed for the first time and can be more preferable for fast-track expert systems in engineering applications. Both approaches are successfully used to determine the optimal parameters of material model in the case of impact of copper and aluminum plates at different temperatures and impact velocities with experimental data from the literature. A simple relaxation plasticity model with a constant relaxation time is used in the present research as an example; more accurate coincidence with the experimental data and wider applicability of the ANN-based approach can be expected for a more realistic plasticity model.

Parameter Identification of Permanent Magnet Synchronous Motor with Dynamic Forgetting Factor Based on H∞ Filtering Algorithm

To address system parameter changes during permanent magnet synchronous motor (PMSM) operation, an H∞ filtering algorithm with a dynamic forgetting factor is proposed for online identification of motor resistance and inductance. First, a standard linear discrete PMSM parameter identification model is established; then, the discrete H∞ filtering algorithm is derived using game theory reducing state and measurement noise influence. A cost function is defined, solving extremes values of different terms. A dynamic forgetting factor is introduced to the weighted combination of initial and current measurement noise covariance matrices, eliminating identification issues from different initial values. On this basis, a dynamic forgetting factor is added to weigh the combination of the initial measurement noise covariance matrix and the current measurement noise covariance matrix, which eliminates the influence of the discrimination error caused by the different initial values. Finally, the identification model is built in MATLAB/Simulink for simulation analysis to verify the feasibility of the proposed algorithm. The simulation results show the proposed H∞ filtering algorithm rapidly and accurately identifies resistance and inductance values with significantly improved robustness. The forgetting factor enables quick stable recognition even with poor initial values, enhancing PMSM control performance.

Identification of Johnson-Cook material model parameters for laser shock peening process simulation for AA2024, Ti–6Al–4V and Inconel 718

This paper addresses the identification of Johnson-Cook material model parameters for the simulation of high strain rate processes such as laser shock peening. A combined numerical and experimental approach is described for the identification of the material parameters for AA2024-T3, Ti–6Al–4V, and Inconel 718 alloys. Validation of the parameters was performed based on depth-resolved residual stress profile evaluation after both experimental and numerical laser shock peening application. However, it has been observed that the strain rate-dependent coefficient identified at low strain rates is insufficient for accurately representing the behavior of the Ti–6Al–4V alloy. Consequently, there is a need to identify and determine the appropriate parameter specifically tailored to the strain rates that are relevant to the intended application of the material. The outcomes of this study provide significant insights into the accurate identification of Johnson-Cook material model parameters for laser shock peening simulations. The findings emphasize the critical importance of considering the strain rate-dependent coefficient C in the Johnson-Cook material model to enhance the accuracy and precision of representing material behavior in simulation efforts.

Dynamic parameter identification based on improved particle swarm optimization and comprehensive excitation trajectory for 6R robotic arm

PurposeRobotic arms’ interactions with the external environment are growing more intricate, demanding higher control precision. This study aims to enhance control precision by establishing a dynamic model through the identification of the dynamic parameters of a self-designed robotic arm.Design/methodology/approachThis study proposes an improved particle swarm optimization (IPSO) method for parameter identification, which comprehensively improves particle initialization diversity, dynamic adjustment of inertia weight, dynamic adjustment of local and global learning factors and global search capabilities. To reduce the number of particles and improve identification accuracy, a step-by-step dynamic parameter identification method was also proposed. Simultaneously, to fully unleash the dynamic characteristics of a robotic arm, and satisfy boundary conditions, a combination of high-order differentiable natural exponential functions and traditional Fourier series is used to develop an excitation trajectory. Finally, an arbitrary verification trajectory was planned using the IPSO to verify the accuracy of the dynamical parameter identification.FindingsExperiments conducted on a self-designed robotic arm validate the proposed parameter identification method. By comparing it with IPSO1, IPSO2, IPSOd and least-square algorithms using the criteria of torque error and root mean square for each joint, the superiority of the IPSO algorithm in parameter identification becomes evident. In this case, the dynamic parameter results of each link are significantly improved.Originality/valueA new parameter identification model was proposed and validated. Based on the experimental results, the stability of the identification results was improved, providing more accurate parameter identification for further applications.

Metaheuristic adaptive control based on polynomial regression and differential evolution for robotic manipulators

Adaptive control of systems consists of the online adjustment of their control parameters to deal with parametric uncertainties and disturbances. Indirect Adaptive Control (IAC) of electromechanical systems and robotic manipulators based on metaheuristic optimization has been shown to be competitive compared to other classical and advanced control schemes. In this approach, the identification of system model parameters is stated as an optimization problem and then solved online by metaheuristics. Subsequently, the identified model is used in a second stage of online metaheuristic optimization to predict the system behavior and tune its control parameters. Therefore, this approach can be computationally expensive and unaffordable for systems with more complex dynamics whose models are just as complex. Due to the above, this work proposes an indirect adaptive control method for robotic manipulators based on differential evolution (DE) optimization, which aims to reduce the computational cost of the online identification stage by using Polynomial Regressors (PR) whose adjustment can be performed through a closed-form solution. The proposed method is tested in simulation for a 2 d.o.f. robotic manipulator and the results are compared against the original indirect adaptive control approach based on metaheuristic optimization and also a controller optimized offline. The post-hoc pair-wise Friedman tests confirm that the proposed approach provides competitive results compared to the original approach, with significant savings in computational resources.