Parameter Identification Approach Research Articles

Measured displacement data analysis and modelling for structural health monitoring

Non-physics-based modeling is gaining attention in the area of structural health monitoring due to underlying advantages e.g. finite-element or physics-based model might not exist at all. Therefore, in case of aforementioned scenario, it may be useful to have a model that is developed via the use of time-series data instead of knowing the exact underlying physics. Such model can be developed using parametric, non-parametric, and system identification approach, where, later, the state-space based model quantities can be derived. In this study, experimentally measured time-series data (e.g. displacement) have been utilized to develop a representative model. Initially, the data has been passed through a screening and selection process including data cleaning and filtering. Subsequently, the filtered data has been validated with the original data to make sure that overall dynamics remain same. Later, cleaned data have been employed to have an illustrative model via employing autoregressive type models. The influence of the model orders have been investigated and a comparison of different results have been presented. Further, the least-squares approach has been adopted to minimizes the prediction errors to optimize overall performance. However, it needs to be mentioned as herein out-only measured data has been used therefore no transfer function can be derived also stabilization plot is not possible without having a transfer function. Finally, the developed models output have been validated in both time and frequency domain. In short, it has been observed that the model output may vary significantly depending on the model orders and noise contamination of the original signal. The investigated approach is beneficial for monitoring while physics-based models either not available or not possible to derive.

Chaos inspired invasive weed optimization algorithm for parameter estimation of solar PV models

High performance solar photovoltaic models require precise knowledge of solar PV cell parameters. Numerous methods based on both deterministic and meta-heuristics have been developed for identifying solar cell parameters. However, the presented methods in the literature have a heavy computational load and limited ability to extract crucial parameters due to nonlinear dynamics of solar PV systems. In addition, because they rely on approximations to determine the objective function, the preceding state-of-the-art parameter estimation techniques do not provide accurate results. Thus, a novel chaos-inspired invasive weed optimization (CIIWO) has been developed for accurate solar PV system parameter estimation. Adding a chaotic map to IWO improves the performance of suggested method by expanding the search space globally. Moreover, to cope with the inadequacy in state-of-art objective functions, Newton Raphson approach has been combined with proposed CIIWO algorithm. The suggested approach for solar cell parametric identification has been tested on one-diode, two-diode, and three-diode models. By contrasting the outcomes with nine contemporary optimization strategies for parameter estimation, the superiority of the suggested algorithm has been demonstrated. Commercial PV cell RTC France has been used for the experimental validation. Comprehensive study of experimental data validates the efficacy and stability of the suggested algorithm.

Enhancing battery management for HEVs and EVs: A hybrid approach for parameter identification and voltage estimation in lithium-ion battery models

In recent years, batteries have evolved increasingly overall in numerous applications. Among batteries, LIBs are the most advantageous technology because of their raised power and energy densities. This study proposes a hybrid method, combining a war strategy optimization (WSO) algorithm and a hierarchical deep learning neural network (HDLNN) named WSO-HDLNN, to identify the parameters of lithium-ion batteries (LIBs) used in hybrid and electric vehicles (HEVs and EVs). The hybrid approach utilizes the WSO technique to generate parameters and predicts the components using the HDLNN approach. The proposed method significantly reduces the estimated voltage and measured voltage error while effectively identifying the battery parameters. The MATLAB/SIMULINK platform is employed for implementation and comparison with other existing methods such as differential evolution (DE), grasshopper optimization algorithm (GOA), and particle swarm optimization (PSO). Simulation results demonstrate the efficiency of the proposed WSO-HDLNN strategy in reducing battery voltage errors by accurately identifying parameters and improving voltage estimation accuracy. Further, notable novelty in this work is the integration of the WSO algorithm with the HDLNN in the WSO-HDLNN protocol for LIB parameter identification. This fusion is distinct as it synergizes the strengths of optimization and deep learning, enhancing efficiency and accuracy in LIB parameter estimation. The WSO algorithm introduces a novel war strategy element, leading to faster convergence to optimal solutions, significantly reducing computational time. Moreover, the WSO-HDLNN approach showcases robustness in handling noisy data, a unique feature ensuring accurate parameter estimates amidst real-world uncertainties, setting it apart from conventional LIB modeling methods.

Dynamic Model-Embedded Intelligent Machine Fault Diagnosis Without Fault Data

Intelligent machine fault diagnosis technique has recently exploded interest in digital health, energy power, and industrial maintenance. Collecting machine fault data in engineering practice is usually costly, causing big challenges in the intelligent diagnosis of most fresh-from-the-factory machines that are missing fault data. Inspired by the main idea of the digital twin, this paper proposes a dynamic model-embedded intelligent machine fault diagnosis framework. Specifically, a machine dynamic modeling and parameter identification approach is introduced for building the machine digital model. The digital model is used to predict machine fault data from the measured healthy vibration signals. Furthermore, a parameterized convolutional neural network structure is designed for learning optimization features and recognizing the machine's healthy state. Experimental investigation demonstrates the effectiveness of the framework. The results show that the proposed framework enables intelligent machine fault diagnosis without fault data. The diagnosis effect outperforms supervised, unsupervised and small-sample learning approaches and can be generalized to another load and speed with acceptable accuracy.

406 A Parameter Identification Approach towards Analyzing Hemodynamicsbased on Capnography

406 A Parameter Identification Approach towards Analyzing Hemodynamicsbased on Capnography

Robust Observer-Based Parameter Identification and State Estimation for COVID-19 Modeling: Analyzing the Omicron Variant in Mexico using Fractional-Order SIR and SEIR Models

This work presents a robust observer-based approach for parameter identification and state estimation in fractional-order SIR and SEIR models, applied to analyze the spread of the SARS-CoV-2 Omicron variant in Mexico from December 26, 2021, to March 26, 2022. By utilizing measurements of changes in the infected population, a robust observer is proposed to reconstruct the transmission rate within the fractional model. The method accounts for noisy measurement cases and incorporates parametric reconstruction and state observation. The proposed approach is demonstrated through numerical results, showcasing its capabilities in effectively estimating parameters and states. The findings of this study have implications for formulating strategies to prevent, slow, and halt the spread of infectious diseases, offering valuable insights for combating future disease outbreaks.

An Improved Differential Evolution for Parameter Identification of Photovoltaic Models

Photovoltaic (PV) systems are crucial for converting solar energy into electricity. Optimization, control, and simulation for PV systems are important for effectively harnessing solar energy. The exactitude of associated model parameters is an important influencing factor in the performance of PV systems. However, PV model parameter extraction is challenging due to parameter variability resulting from the change in different environmental conditions and equipment factors. Existing parameter identification approaches usually struggle to calculate precise solutions. For this reason, this paper presents an improved differential evolution algorithm, which integrates a collaboration mechanism of dual mutation strategies and an orientation guidance mechanism, called DODE. This collaboration mechanism adaptively assigns mutation strategies to different individuals at different stages to balance exploration and exploitation capabilities. Moreover, an orientation guidance mechanism is proposed to use the information of the movement direction of the population centroid to guide the evolution of elite individuals, preventing them from being trapped in local optima and guiding the population towards a local search. To assess the effectiveness of DODE, comparison experiments were conducted on six different PV models, i.e., the single, double, and triple diode models, and three other commercial PV modules, against ten other excellent meta-heuristic algorithms. For these models, the proposed DODE outperformed other algorithms, with the separate optimal root mean square error values of 9.86021877891317 × 10−4, 9.82484851784979 × 10−4, 9.82484851784993 × 10−4, 2.42507486809489 × 10−3, 1.72981370994064 × 10−3, and 1.66006031250846 × 10−2. Additionally, results obtained from statistical analysis confirm the remarkable competitive superiorities of DODE on convergence rate, stability, and reliability compared with other methods for PV model parameter identification.

Time-varying structural parameter identification for the offshore gangway

The offshore gangway is a new type of offshore transfer equipment, equivalent to an offshore passage. This device makes it convenient for people to transfer between a vessel and an offshore structure. The control algorithm of the gangway system demands adjusting its control parameters to deal with harsh sea conditions and ensure personnel safety. Thence, it is essential to foreknow its time-varying structural parameters, such as the moment of inertia, the mass, the location of the mass center, and so on. However, due to the highly complex structure, these parameters are difficult to obtain through measurement directly. To adaptively identify these time-varying parameters for better controller design, this paper proposes a novel structural parameter identification approach based on the idea of interval division. During the identification process, the entire period is uniformly divided into several intervals. The time-varying model of the offshore gangway system can be represented in each interval by a quadratic regression equation, the coefficients of which can be derived by the least-squares algorithm. Furthermore, to adapt to the different variation frequencies and amplitudes of the parameters, the divided intervals are of distinct sizes according to the characteristics of each identification parameter. In addition, the particle swarm algorithm is employed to obtain the optimal interval sizes, the cost function of which consists of a series of output error terms. Verified by simulation, the method presented in this paper can effectively estimate the structural parameters of the offshore gangway with high accuracy, with mean square errors reduced by at least 2/3 compared to other methods proposed in previous research.

A Robust Parameter Identification Approach with Anti-Outlier Characteristics for Lithium-Ion Batteries

Accurate acquisition of model parameters of lithium-ion batteries (LIBs) is imperative for precious estimation state of charge. However, due to the interference of noise, the inevitable uncertainty may lead to outliers appearing in measurement data, which often leads to inaccurate parameter identification. To solve the problem, a robust Kalman filter (RKF) that uses the Mahalanobis Distance Criterion (MDC) is proposed for the parameter identification of LIBs. Firstly, the one-order equivalent circuit model of the LIBs is created. Subsequently, the gain of robust factor is implemented into the Kalman filter to decrease filter gain and swell the measurement noise covariance when facing the outlier measurements. Finally, the dynamic stress test condition of the electric vehicle is used to prove the effectiveness of the RKF-MDC approach. The simulation reveals that the RKF-MDC can reduce the influence of outlier values for the parameter identification of LIBs.

Joint Coefficient and Solution Estimation for the 1-D Wave Equation: An Observer-Based Solution to Inverse Problems

The estimation of an unknown source coefficient and of the solution to a 1-dimensional (1D) wave equation via exponentially convergent state observers is the problem under consideration in this work. The coefficient is assumed to depend on the space variable only and to be polynomial. The main observation information for this inverse problem is the value of the solution to the wave equation in a subinterval of the domain, including also some of its higher-order spatial derivatives. In order to estimate the source coefficient, we turn it into a new state as in finite-dimensional parameter identification approaches. However in this infinite-dimensional setting, this requires the introduction of a novel indirect approach involving an infinite-dimensional state transformation. Sufficient conditions allow the design of a composite observer consisting of an internal observer, which estimates in higher regularity spatial norms both the source term and the solution on a subinterval, and a boundary observer, in order to eventually provide the estimation of the solution everywhere. The observer convergence is proven by means of Lyapunov analysis. An extension of this approach to the case of the identification of a potential in the wave equation is finally considered, which is a nonlinear inverse problem since here, in the equation, the unknown coefficient multiplies the solution.

Experimental and numerical investigations for compressive behavior of porous hydroxyapatite bone scaffold fabricated via freeze-gel casting method

Artificial bone scaffolds with a porosity of ≥64% were manufactured using hydroxyapatite (HA). The Freeze-gel Casting method using tert-butyl alcohol (TBA) as a solvent was adopted to maintain proper mechanical strength. The manufactured HA scaffold had excellent internal connectivity owing to the continuous connection of the long columnar pores and arrangement of the constant pore walls. For the HA scaffold, compression tests at different strain rates under uniaxially compressed loads were performed, and the compressive behavior according to the strain rate was analyzed. A maximum yield stress of 2–3 MPa was observed, and the overall compressive behavior was divided into elastic, plateau, and densification sections, which were different from the brittleness behavior of ordinary ceramics and similar to that of polymer materials. The compressive behavior of the HA scaffold was successfully simulated by applying the Frank–Brockman–Zairi constitutive model used to simulate elasto-viscoplastic material behavior. Seven material parameters belonging to the constitutive equation were proposed for the HA scaffold by adopting a deterministic approach for material parameter identification. Models can be developed from methods for simulating the mechanical behavior of a porous ceramic scaffold to predict the long-term mechanical behavior of the scaffold inserted in vivo, which may help in bone defects recovery.

High-cycle fatigue model calibration with a deterministic optimization approach

A parameter identification approach is proposed to calibrate the Ottosen high cycle fatigue model using numerical optimization with regularization. The damage evolution was predicted by a continuum approach based on a moving endurance surface in the stress space, so the stress states outside the endurance surface may lead to damage evolution. The calibration of the model relied on uniaxial and multiaxial experimental data. The predictions of the calibrated models were in fair agreement with the experimental data for the 7075-T7451 and 7050-T6 aluminum alloys subjected to cyclic uniaxial and multiaxial loadings.

Parameter Identification of Model for Piezoelectric Actuators.

Piezoelectric actuators are widely used in high-precision positioning systems. The nonlinear characteristics of piezoelectric actuators, such as multi-valued mapping and frequency-dependent hysteresis, severely limit the advancement of the positioning system's accuracy. Therefore, a particle swarm genetic hybrid parameter identification method is proposed by combining the directivity of the particle swarm optimization algorithm and the genetic random characteristics of the genetic algorithm. Thus, the global search and optimization abilities of the parameter identification approach are improved, and the problems, including the genetic algorithm's poor local search capability and the particle swarm optimization algorithm's ease of falling into local optimal solutions, are resolved. The nonlinear hysteretic model of piezoelectric actuators is established based on the hybrid parameter identification algorithm proposed in this paper. The output of the model of the piezoelectric actuator is in accordance with the real output obtained from the experiments, and the root mean square error is only 0.029423 μm. The experimental and simulation results show that the model of piezoelectric actuators established by the proposed identification method can describe the multi-valued mapping and frequency-dependent nonlinear hysteresis characteristics of piezoelectric actuators.

Editorial for the special issue “Control‐theoretic approaches for systems in the life sciences”

Editorial for the special issue “Control<scp>‐theoretic</scp> approaches for systems in the life sciences”

Hybrid approach for parameter identification of the two-diode model of photovoltaic modules

PurposeParameter identification of photovoltaic (PV) modules plays a vital role in modeling PV systems. This study aims to propose a novel hybrid approach to identify the seven parameters of the two-diode model of PV modules with high accuracy.Design/methodology/approachThe proposed hybrid approach combines an improved particle swarm optimization (IPSO) algorithm with an analytical approach. Three parameters are optimized using IPSO, whereas the other four are analytically determined. To improve the performance of IPSO, three improvements are adopted, that is, evaluating the particles with two evaluation functions, adaptive evolutionary learning and adaptive mutation.FindingsThe performance of proposed approach is first verified by comparing with several well-established algorithms for two case studies. Then, the proposed method is applied to extract the seven parameters of CSUN340-72M under different operating conditions. The comprehensively experimental results and comparison with other methods verify the effectiveness and precision of the proposed method. Furthermore, the performance of IPSO is evaluated against that of several popular intelligent algorithms. The results indicate that IPSO obtains the best performance in terms of the accuracy and robustness.Originality/valueAn improved hybrid approach for parameter identification of the two-diode model of PV modules is proposed. The proposed approach considers the recombination saturation current of the p–n junction in the depletion region and makes no assumptions or ignores certain parameters, which results in higher precision. The proposed method can be applied to the modeling and simulation for research and development of PV systems.

A Bayesian inference approach for parametric identification through optimal control method

AbstractThe objective of the research is to obtain deterministic and probabilistic resolution of material parameters from full field measurements of kinematic data acquired from digital image correlation. The deterministic inverse problem involves formulation of an optimal control approach where the complete knowledge of the boundary conditions and the measurement data are not required. The probabilistic framework inculcates this optimal control method in a Bayesian inference framework. A Markov chain Monte Carlo sampling method is applied to obtain the posterior probability density function, along with a radial basis function network for the numerical frugality of the samplings.

Path planning algorithm based on adaptive model predictive control and learning-based parameter identification approach for incremental forming processes for product precision improvements

Path planning algorithm based on adaptive model predictive control and learning-based parameter identification approach for incremental forming processes for product precision improvements

On the Design of An Equivalent Circuit Model for Lithium-Ion Batteries Operating Across Broad Current Ranges

Equivalent circuit models have gained significant use in lithium-ion battery management systems, because of their computational efficiency and convenience for use. However, none of the existing ones by design is suitable for charging/discharging across low to high Crates. In this paper, we propose a new equivalent circuit model, called BattX, to address the challenge. The BattX model develops and combines circuit analogs to not only simulate the main dynamic processes in the electrode and electrolyte phases and in the temperature evolution, but also capture their effects on the terminal voltage. The design presents a correspondence with electrochemical modeling to comprehensively grasp a battery's dynamic behavior, thus ensuring a predictive capability over broad C-rate ranges. We further present a multi-pronged parameter identification approach to extract the model's parameters from measurement data. Extensive simulations involving different scenarios and load profiles are conducted to show the model's high predictive accuracy when the current ranges are wide.

Parameter identification approach using improved teaching and learning based optimization for hub motor considering temperature rise

Parameter identification approach using improved teaching and learning based optimization for hub motor considering temperature rise

Estimation of Soil–Structure Model Parameters for the Millikan Library Building Using a Sequential Bayesian Finite Element Model Updating Technique

We present a finite element model updating technique for soil–structure system identification of the Millikan Library building using the seismic data recorded during the 2002 Yorba Linda earthquake. A detailed finite element (FE) model of the Millikan Library building is developed in OpenSees and updated using a sequential Bayesian estimation approach for joint parameter and input identification. A two-step system identification approach is devised. First, the fixed-base structural model is updated to estimate the structural model parameters (including effective elastic modulus of structural components, distributed floor mass, and Rayleigh damping parameters) and some uncertain components of the foundation-level motion. Then, the identified structural model is used for soil–structure model updating wherein the Rayleigh damping parameters, the stiffness and viscosity of the soil subsystem (modeled using a substructure approach), and the foundation input motions (FIMs) are estimated. The identified model parameters are compared with state-of-practice recommendations. While a specific application is made for the Millikan Library, the present work offers a framework for integrating large-scale FE models with measurement data for model inversion. By utilizing this framework for different civil structures and earthquake records, key structural model parameters can be estimated from the real-world recorded data, which can subsequently be used for assessing and improving, as necessary, state-of-the-art seismic analysis and structural modeling techniques. This paper presents an effort towards using real-world measurements for large-scale FE model updating in the soil and structure, uniform soil time domain for joint parameter and input estimation, and thus paves the way for future applications in system identification, health monitoring, and diagnosis of civil structures.