Parameter Identification Approach Research Articles

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.

An improved PSO-based approach for the photovoltaic cell parameters identification in a single diode model

An improved PSO-based approach for the photovoltaic cell parameters identification in a single diode model

A holistic water quality risk assessment and key parameter identification approach coupling comparative water quality index (WQI) models: a six-year monitoring study in Danjiangkou Reservoir, China

A holistic water quality risk assessment and key parameter identification approach coupling comparative water quality index (WQI) models: a six-year monitoring study in Danjiangkou Reservoir, China

Experimental characterization of acoustic damping materials

AbstractDue to their ease of use and low cost, passive damping methods are a preferred mean for the reduction of noise in many engineering applications. This applies in particular to foam materials, which exhibit good acoustic and mechanical damping properties. The selection of suitable materials is usually carried out experimentally and can be very labor‐intensive and time‐consuming. For this reason, it is helpful to develop qualified numerical methodsthat can be used for material design and selection. Hence, foam materials must be characterized experimentally in order to enable a later comparison with vibroacoustic simulations. This contribution, therefore, aims at providing suitable parameters for the use in numerical analyses and their validation. Firstly, the microstructure of a foam specimen is captured by means of a CT scan. In addition to providing the geometry for the multi‐physics simulations, this measurement is also used to determine characteristic foam features, for example, strut thickness and pore size distribution. In the second step, the frequency‐dependent stiffness and damping properties of the material are determined by a special experimental setup utilizing an electrodynamic shaker. Here, the dynamic system is approximated as a single‐mass oscillator, which is sufficiently accurate for low frequencies. These properties will later be used in the numerical model to evaluate different parameter identification approaches. In the third and last step of the experimental campaign, measurements with an impedance tube are conducted to obtain the coefficient of absorption. This material parameter is particularly suitable for comparing experiments and simulations. Finally, the correlation between the experimental results is examined to provide a deeper understanding of the foam materials.

Automated identification and long-term tracking of modal parameters for a super high-rise building

Structural modal parameter identification plays an important role in wind resistance design, damage identification, and health monitoring. At present, the modal identification process still suffers from low automation and low computational efficiency, which are not conducive to the continuous processing of monitoring data. To address this, an automated modal parameter identification approach is proposed by introducing the fast density and grid based (DGB) clustering, and applied to the Shanghai Tower, which is the tallest building in China. Moreover, in order to determine the optimal model order of the stochastic subspace identification (SSI), the trend of the logarithmic reduction rate for singular values is extracted by empirical mode decomposition (EMD). The numerical simulation of a cantilever beam and the field measurement of Shanghai Tower under the influence of Typhoon In-Fa are conducted to validate the effectiveness of the proposed approach. Results demonstrate that the approach can obtain modal parameters without human intervention and has the advantages of simple process, fast computation and high precision. Furthermore, based on the data collected from the structural health monitoring system of the Shanghai Tower over a period of 5.5 years, the approach is applied to analyze the correlation between natural frequencies and ambient temperature, as well as the long-term evolution law of modes, discovering that multi-order frequencies show a decreasing trend over time. The finite element analysis suggests that this fact might be attributed to the increase in structural mass caused by the rising usage rate and the degradation in material properties.

Vector-autoregressive based vibration monitoring of wind energy turbines using laser scanner profile measurements

Structural health monitoring of wind energy turbines (WET) is crucial due to the potential presence of an imbalanced rotor, leading to adverse centrifugal forces. In this research, non-contact vibration monitoring of a WET is conducted using a terrestrial laser scanner (TLS) of the type Zoller+Fröhlich IMAGER 5016 in a two-dimensional (2D) profile mode, and with a rotation speed of 55 revolutions per second. The TLS profile measurements cover the entire pillar up to a height of 100 meter. Therefore, the challenges imposed by conventional measurement systems, such as accelerometers or inductive displacement transducers, are tackled through the high spatial resolution of the TLS measurements and non-contact measurement methods. Additionally, both time and cost are reduced regarding the sensor installation. In general, the WET is measured in two different directions to account for significant movements, both in the direction of the wind and perpendicular to it. To initiate the analysis, time series are generated from the profile measurements for various positions covering the entire pillar. A robust time-domain modal parameter identification approach based on the Vector-autoregressive (VAR) process with multivariate t-distributed random deviations (VAR-RT-MPI) is proposed to estimate modal parameters, including eigenfrequencies, eigenforms, and modal damping. Besides, it allows estimating unknown auto- and cross-correlation coefficients of the VAR process, the cofactor matrix, and the degrees of freedom of the t-distribution. The VAR-RT-MPI algorithm enables to jointly estimate the eigenfrequencies and damping ratio coefficients considering multiple time series. Additionally, the spatio-temporal model of the pillar is characterised based on the estimates of the amplitudes (in a submillimetre range) and phase shifts at different positions. Vibration monitoring was conducted on a WET located in Hannover, Germany, and the results were compared with a well-known covariance driven stochastic subspace identification (SSI-COV) approach.

An Actuation Acceleration-Based Kinematic Modeling and Parameter Identification Approach for a Six-Degrees-of-Freedom 6-PSU Parallel Robot With Joint Clearances

Abstract The 6-prismatic-spherical-universal (6-PSU) parallel robot is useful for high-accuracy positioning, where the motors are installed on the robot base and the moving parts of the robot have low inertia. A highly accurate kinematic model of the robot is fundamental for the control. The joint clearances of all limbs often exist and have a significant influence on kinematic model accuracy. In this study, an actuation acceleration information-based kinematic modeling and identification method for a 6-PSU parallel robot with joint clearances is proposed. The direction of the joint clearance is related to the direction of the force acted on the joint, which is determined by the acceleration of the prismatic actuator. The existence of joint clearances is equivalent to the link length change. The joint clearances are identified from the experiments and compensated. Simulations and experiments show that the proposed method is effective and improves the accuracy of the kinematic model.

Application of a Bayesian-Based Integrated Approach for Groundwater Contamination Sources Parameter Identification Considering Observation Error

Groundwater contamination source (GCS) parameter identification can help with controlling groundwater contamination. It is proverbial that groundwater contamination concentration observation errors have a significant impact on identification results, but few studies have adequately quantified the specific impact of the errors in contamination concentration observations on identification results. For this reason, this study developed a Bayesian-based integrated approach, which integrated Markov chain Monte Carlo (MCMC), relative entropy (RE), Multi-Layer Perceptron (MLP), and the surrogate model, to identify the unknown GCS parameters while quantifying the specific impact of the observation errors on identification results. Firstly, different contamination concentration observation error situations were set for subsequent research. Then, the Bayesian inversion approach based on MCMC was used for GCS parameter identification for different error situations. Finally, RE was applied to quantify the differences in the identification results of each GCS parameter under different error situations. Meanwhile, MLP was utilized to build a surrogate model to replace the original groundwater numerical simulation model in the GCS parameter identification processes of these error situations, which was to reduce the computational time and load. The developed approach was applied to two hypothetical numerical case studies involving homogeneous and heterogeneous cases. The results showed that RE could effectively quantify the differences caused by contamination concentration observation errors, and the changing trends of the RE values for GCS parameters were directly related to their sensitivity. The established MLP surrogate model could significantly reduce the computational load and time for GCS parameter identification. Overall, this study highlights that the developed approach represents a promising solution for GCS parameter identification considering observation errors.

A Parameter Identification Approach in Inductive Power Transfer Systems for Light Electric Vehicles

A Parameter Identification Approach in Inductive Power Transfer Systems for Light Electric Vehicles

Remaining useful life prediction for stochastic degrading devices incorporating quantization

Quantization has been widely employed in analog-to-digital conversion (ADC) for the acquisition of digital data, which are further utilized for prognostics. However, quantization errors are inevitable during ADC, resulting in bias in the subsequent prognosis results. Compared to the numerous researches on prognosis considering measurement noises, slight attention has been paid on remaining useful life (RUL) for degrading devices incorporating quantization errors. In this study, a Wiener-process-based model incorporating mixed random noise is utilized to describe the degradation process involving quantization errors. In order to mitigate the impact of quantization errors, a parameter identification approach and a degradation state estimation method are proposed, which integrate maximum likelihood estimation, particle filter, and Bayesian inference. Subsequently, the results of RUL prediction with and without considering quantization errors are derived, respectively. Finally, numerical examples and a case study of the control moment gyroscopes (CMG) and batteries are provided to demonstrate the proposed method.

POE-Based Error Modeling and Multiple Plane Constraint-Based Parameter Identification for the Kinematic Calibration of a 4-UPS/SPR Parallel External Fixator

Limb deformity is a common complaint in orthopedic surgery. Currently, gradual treatment using parallel external fixator (PEF) has become the preferred option for deformity correction. As the key medical apparatus with the special application properties of temporary assembly and direct utilization, the geometric errors generated during the manufacturing and assembly process of the PEF contribute to the post-correction residual and iatrogenic deformities. However, considering the complexity of clinical scenarios, there is a lack of an applicable solution to reduce the effect of the above error source of PEF. To overcome these problems, taking a 4-UPS/SPR PEF as the research object, this paper proposes a novel kinematic calibration method. First, the error model with the features of completeness, continuity, and minimality is first established based on the product of exponentials (POE) formula. Then, the identifiability analysis is conducted to eliminate the redundant error parameters and ensure the stability and accuracy of the iterative calculation in the process of parameter identification. Furthermore, a multiple plane constraint (MPC)-based parameter identification approach is introduced to the calibration task of the PEF to effectively reduce the difficulty of the pose information measurement in the current cost-effective constraint calibration method. Finally, the simulation and prototype experiments validate that the proposed calibration method (i.e., the combination of the POE-based error model and the MPC-based parameter identification approach) can accomplish the calibration tasks in clinical applications and meet the cost-effectiveness and efficiency requirements.

Novel approach for sheet metal constitutive parameters identification based on shape index and multiple regression

To accurately predict the behavior and mechanical damage of sheet metal during hot forming process, the use of advanced thermomechanical modeling and precise identification of the constitutive parameter are required. This paper proposes a novel identification approach for constitutive parameters that relies on multiple regression and the shape index of experimental data curves. This approach is used to determine the material constants associated with the Johnson-Cook material and failure models for steel and aluminum sheets. Therefore, the obtained results approve the applicability and high accuracy of the identification procedure for the damage and the constitutive parameters of sheet metal, which allows for substantial time savings. Furthermore, to evaluate the formability of sheet metal, experimental forming tests at various temperatures were conducted. The identification process enhances the prediction of forming results, as shown by the fact that the numerical simulation of Erichsen and square die hydroforming tests using identified Johnson-Cook parameters agreed with the results of these tests carried out at different forming conditions. Moreover, the thickness measurement shows the ability of the developed model to properly predict the thickness of deformed shapes.

A Self-Optimized Machine Learning Approach for Constitutive Parameters Identification of Aortic Walls

The accurate identification of constitutive parameters is considered the key challenge for the study of mechanical properties of biological soft tissues. The popular machine learning (ML)-based inverse identification frameworks always require large amounts of training datasets. This work proposes an ML framework called self-optimized ML for accurate identifying the constitutive parameters of aortic walls under few training datasets conditions. The self-optimized ML includes three steps: Step 1: the forward physical FEM models first simulate the nonlinear deformation of aortic walls subject to uniaxial tension tests and are used to establish the nonlinear relationship datasets, Step 2: the carefully designed inverse random forest (RF) of the ML model can offer rapid identification by learning the established nonlinear relationship datasets, and Step 3: forward physical FEM models are recalled to evaluate the error between the identification results in Step 2 and real values, and then the accuracy are embedded into RF for guiding optimization directions to ensure that the final identification results are accurately and physically reasonable. The accuracy and robustness validation of proposed framework was conducted by constitutive parameters identification of uniaxial tension experiment samples of bovine aortic walls. The approach proposed achieves the R-squared exceeding 96.90% in longitudinal direction and 98.30% in circumferential direction, which is better than directly ML approach and gradient-based approach under the same amounts of datasets, respectively. The comparison results show that self-optimized ML can not only achieve accurate identification of the constitutive parameters of aortic walls, but also can decrease the identification results dependency of the initial number of sampling data effectively. The identification approach developed herein provides a common and convenient framework for constitutive parameters identification of biological soft tissues.

Modal frequency identification using the predictor subspace approach for a space solar power station

The system’s modal frequency parameters are determined by using a technique known as predictor-based subspace identification (PBSID), which is based on the dynamic properties of space solar power stations’ huge size and low frequency. First, the dynamic equation for the space solar power station with an Abacus configuration’s attitude-vibration coupling is developed. Additionally, the PBSID approach is used to build the relevant parameter matrix, and singular value decomposition (SVD) is chosen to evaluate the system’s structural frequency parameters. The right input signals are created via numerical simulation, and the best sensor placement technique yields the associated vibration response signals. The computing results then demonstrate that the modal frequency characteristics of the space solar power station may be successfully identified by the PBSID method, which is based on SVD. Additionally, the findings demonstrate the superior noise immunity capabilities of the PBSID algorithm when compared to the standard modal parameter identification approaches.

Practical fixed‐time composite‐learning control for full‐state constraint strict‐feedback non‐linear systems: A dynamic regressor extension and mixing based approach

AbstractA practical fixed‐time composite learning control scheme, by combining dynamic regressor extension and mixing (DREM) parameter identification algorithm and adaptive dynamic surface control (DSC) technique, is proposed for a class of strict‐feedback non‐linear systems subjected to linear‐in‐parameters uncertainties and full‐state constraint. To address the problem of state constraint, a non‐linear transformation function is introduced to convert the originally constrained non‐linear system into an unconstrained one. Meanwhile, the hyperbolic tangent function is employed to avoid singularity issues that often appeared in the traditional fixed‐time (FXT) control designs. In order to relax the requirement of persistency of excitation condition, a modified FXT‐DREM parameter identification approach with an interval excitation condition is constructed by introducing a three‐layer transformation technique derived from the classical DREM algorithm. Then, the modified FXT‐DREM parameter identification algorithm is seamlessly integrated into the adaptive DSC framework, resulting in a composite‐learning control scheme. By employing Lyapunov stability analysis, the fixed‐time convergence of both the parameter estimation error and the trajectory tracking error is proved. Finally, the effectiveness of the proposed design is demonstrated through simulation test.

Self-adaptive enhanced learning differential evolution with surprisingly efficient decomposition approach for parameter identification of photovoltaic models

In order for the photovoltaic power generation system to convert energy with the highest efficiency after installation, the unknown parameters of the photovoltaic model need to be accurately set in advance. Therefore, in order to identify unknown parameters of photovoltaic models more reliably, effectively, and accurately, this paper first uses the decomposition approach that is extremely effective for different types of photovoltaic models, called matrix rotation decomposition. The unknown parameters of different photovoltaic models are decomposed into linear parameters and nonlinear parameters through matrix rotation decomposition approach. Then a simple and effective algorithm—Self-adaptive enhanced learning differential evolution (SaELDE)—is proposed to identify the decomposed nonlinear parameters. In SaELDE, there are three improvements: (1) the classification mutation learning mechanism is designed to make classified individuals evolve towards the promising direction; (2) the introduced crossover rate sorting approach effectively makes up for the neglected relationship between individuals and crossover rates; (3) combined with the population reduction strategy, it avoids the impact of the initial population setting on algorithm performance and balances the transition between the exploration and exploitation phases. Finally, the linear parameters are calculated by calculating the derived matrix equations according to the nonlinear parameters. Experimental results from a variety of common photovoltaic models, and even photovoltaic module models that need to face different temperatures and irradiance, show that the proposed SaELDE performs more accurately and has better robustness. In addition, the correlation coefficient of SaELDE is 1 under all conditions, which further proves the accuracy of the parameters extracted by SaELDE.

A Parameter Identification Approach towards Hemodynamics based on Capnography

Research question Pulse detection decisions during cardiopulmonary resuscitation (CPR) are commonly augmented by end-tidal CO2-concentration (etCO2) levels and trend as etCO2 is a surrogate measure of systemic blood flow during (CPR)1. However, ventilatory and circulatory parameters influence the interpretability of etCO2 heavily. Recently developed methods of continuous cardiac output measurement based on capnography are not applicable during CPR due to the required controlled ventilation settings2. In this study, a parameter identification for a model of CO2 transport during resuscitation is used to estimate cardiac output based on capnography and tidal flow. Methodology After development of a compartmental ordinary differential equation (ODE) model to represent CO2 transport during CPR, parameters such as airway dead space and the level of systemic perfusion are estimated by a multishooting parameter identification approach3. To evaluate the method, tests are conducted on experimental data from a porcine model (approved by an ethics committee vote of the Austrian Federal Ministry of Education, Science and Research (GZ: 2021-0.895.386)). The estimated level of systemic perfusion is compared visually to invasively measured mean arterial pressure due to a lack of continuous cardiac output measurements as a ground truth. Results For exemplary data from a female pig (weight 50.4 kg), airway resistance was estimated to 20.8 hPa s /L, compliance: 25.2 mL/hPa, and dead space: 109 mL. Changes in cardiac output can be identified qualitatively as shown in Fig. 1. Conclusion A validated, simple ODE model for CO2 extraction during CPR could contribute quantifying the impact of ventilatory parameters on etCO2 and elucidate CO2 production, storage and transport during cardiac arrest. Assumptions on CO2 production during cardiac arrest and redistribution in the body, and the lacking cardiac output measurements complicate the analysis. Conflicts of Interest All authors have no conflict of interest to declare.

Data-Driven Accelerated Parameter Identification for Chaboche-Type Visco-Plastic Material Models to Describe the Relaxation Behavior of Copper Alloys

Abstract Background Calibrating material models to experimental measurements is crucial for realistic computational analysis of components. For complex material models, however, optimization-based identification procedures can become time-consuming, particularly if the optimization problem is ill-posed. Objective The objective of this paper is to assess the feasibility of using machine learning to identify the parameters of a Chaboche-type material model that describes copper alloys. Specifically, we apply and analyze this identification approach using short-term uniaxial relaxation tests on a C19010 copper alloy. Methods A genetic algorithm forms the basis for identifying the parameters of the Chaboche-type material model. The approach is accelerated by replacing the numerical simulation of the experimental setup by a neural network surrogate. The neural networks-based approach is compared against a classic approach using both, synthetic and experimental data. Results The results show that on the one hand, a sufficiently accurate identification of the material model parameters can be achieved by a classic but time-consuming genetic algorithm. On the other hand, it is shown that machine learning enables a much more time-efficient identification procedure, however, suffering from the ill-posedness of the identification problem. Conclusion Compared to classic parameter identification approaches, machine learning techniques can significantly accelerate the identification procedure for parameters of Chaboche-type material models with acceptable loss of accuracy.

An extended modal approach for modal parameter identification of structure under the existence of harmonic excitations

The existing operational modal analysis (OMA) methods for the structure subjected to white noise and harmonic combined excitations have some limitations such as easy misjudgment of true and spurious modes and slow identification speed. Through expressing the system response with the extended modal matrix (EMM) and the extended modal response (EMR) vector, a novel operational modal parameter identification approach called the extended modal approach is proposed to obtain structural true modes under the existence of multiple harmonic excitations. The EMRs consist of the true modal responses and the spurious modal responses, and each of the former is random while each of the latter is harmonic. It is also proved theoretically that the EMM can be identified using the power spectrum density transmissibility (PSDT) method. Therefore, the extended modal approach includes three steps: (1) Using the PSDT to identify the EMM; (2) Utilizing the least squares reconstruction method to obtain the EMRs from the underdetermined extended modal equation; (3) Judging each of the EMRs to be true or spurious according to its empirical density function. Structural true modes can be ultimately obtained by removing all spurious modes from the identified results. The new approach is numerically verified through the OMA of two multiple-degree-of-freedom systems and then experimentally verified through the OMA of a test beam. The results in numerical simulation and experiments all show that the extended modal approach can identify accurately and quickly structural true modes under multiple harmonic excitations. Besides, the approach has good robustness against noise contamination.

Electrochemical Parameter Identification for Lithium-Ion Battery Sources in Self-Sustained Transportation Energy Systems

Lithium-ion battery (LIB) sources have played an essential role in self-sustained transportation energy systems and have been widely deployed in the last few years. To realize reliable battery maintenance, identifying its electrochemical parameters is necessary. However, the battery model contains many parameters while the measurable states are only the current and voltage, inducing the identification inherently an ill-conditioned problem. A parameter identification approach is proposed, including the experiment, model, and algorithm. Electrochemical parameters are first grouped manually based on the physical properties and assigned to two sequenced tests for identification. The two tests named the quasi-static test and the dynamic test, are compressed on time for practical implementation. Proper optimization models and a sensitivity-oriented stepwise (SSO) optimization algorithm are developed to search for the optimal parameters efficiently. Typically, the Sobol method is applied to conduct the sensitivity analysis. Based on the sensitivity indexes, the SSO algorithm can decouple the mixed impacts of different parameters during the identification. For validation, numerical experiments on a typical NCM811 battery at different life stages are conducted. The proposed approach saves about half the time finding the proper parameter value. The identification accuracy of crucial parameters related to battery degradation can exceed 95%. Case study results indicate that the identified parameters can not only improve the accuracy of the battery model but also be used as the indicator of the battery SOH.