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Purpose: To improve the simulation accuracy of the static characteristics of rubber under multiaxial loading, this study proposed a method to determine the parameters of the constitutive model of rubber under such conditions. Methods: Initially, a multiaxial loading test platform was developed to verify the effect of multiaxial loading on the static properties of rubber. Subsequently, considering both the interval uncertainty of the applied loads and the stochastic uncertainty of measurement noise, a new method combining Bayesian inference, interval analysis, and response surface modeling was used to develop a Mooney-Rivlin constitutive parameter identification model. The constitutive parameters C01 and C10 were successfully predicted using this method, validated using finite element simulations and experiments, and the errors were calculated. Finally, the effects of multiaxial loading (Z-direction preloading and X/Y-direction working loading) and measurement noise on the identification process were discussed. Results: The results indicated that the static stiffness rises in all axial directions as the preload level increased. The intrinsic parameters identified by considering preload ensured that the error in the experimental and simulated stiffness values remained within 15% in all directions compared to the case without considering preload. It was found that considering preload significantly improves the accuracy of static stiffness prediction. [ABSTRACT FROM AUTHOR]

Accurate parameters identification of photovoltaic(PV) models is essential for state assessment of PV systems, as well as for supporting maximum power point tracking and system control, thus holding significant importance. To precisely identify parameters of different PV models, this paper proposes an improved JAYA algorithm based on self-adaptive method, termed Sjaya. Sjaya incorporates three position update strategies, all utilizing adaptive factors, automatically transitioning from explorative to exploitative behaviors, enhancing the population’s ability to escape local optima in the solution space and avoiding premature convergence. The first strategy involves learning towards the best and worst individuals in the population, with the individual iteration direction perturbed by adaptive and normal distribution probability factors to enhance population exploration. The second strategy entails learning towards superior and inferior subgroups, effectively leveraging information from the population, with the ranges of these two subgroups continuously evolving throughout the iteration process. In the third strategy, a novel individual selection mechanism is devised, allocating selection probabilities to individuals based on the exploration phase. Individual updates entail learning from three selected individuals within the population, thereby enhancing population diversity. The proposed Sjaya method is employed to address the parameters identification problem of single diode, double diode, and photovoltaic module models of various photovoltaic types. In numerical experiments, each algorithm was tested 30 times. The average root mean square error (RMSE) of Sjaya for the single diode model and double diode model of RTC France were 9.86022E-04 and 9.849674E-04, respectively. In addition, we use three PV modules to detect Sjaya and competing algorithms. The RMSE of Sjaya on the Photo Watt-PWP 201 module, STM6-40/36 module and STP6-120/36 module is 2.431177E-03, 1.772275E-03 and 1.568231E-02 respectively. The synthesis of experimental findings and analysis indicates that Sjaya outperforms other methods in terms of competitiveness, while also demonstrating high effectiveness and robustness. [ABSTRACT FROM AUTHOR]

To describe materials deformation with viscoplastic characteristics, the Anand model is usually used. For a certain material, the classical method based on the saturation stress of material deformation is usually adopted to identify the parameters in its Anand model. However, for some materials, the saturation stress can not be obtained in the measurable strain range of general experiment equipment, and the classical method should be modified for this situation. Hence in this paper, a method used to identify the saturation stress of material from the insaturation state of deformation is proposed. For the proposed method, the identification of saturation stress is transferred to a problem of obtaining the solution of an objective function, which is derived from the stress-strain curves of insaturation state. By combining the proposed method, the classical Anand parameters identification method can be used in a wider range than before. [ABSTRACT FROM AUTHOR]

Since installing solid oxide fuel cells (SOFCs)-based systems suffers from high expenses, accurate and reliable modeling is heavily demanded to detect any design issue prior to the system establishment. However, such mathematical models comprise certain unknowns that should be properly estimated to effectively describe the actual operation of SOFCs. Accordingly, due to their recent promising achievements, a tremendous number of metaheuristic optimizers (MHOs) have been utilized to handle this task. Hence, this effort targets providing a novel thorough review of the most recent MHOs applied to define the ungiven parameters of SOFCs stacks. Specifically, among over 300 attempts, only 175 articles are reported, where thirty up-to-date MHOs from the last five years are comprehensively illustrated. Particularly, the discussed MHOs are classified according to their behavior into; evolutionary-based, physics-based, swarm-based, and nature-based algorithms. Each is touched with a brief of their inspiration, features, merits, and demerits, along with their results in SOFC parameters determination. Furthermore, an overall platform is constructed where the reader can easily investigate each algorithm individually in terms of its governing factors, besides, the simulation circumstances related to the studied SOFC test cases. Over and above, numerical simulations are also introduced for commercial SOFCs’ stacks to evaluate the proposed MHOs-based methodology. Moreover, the mathematical formulation of various assessment criteria is systematically presented. After all, some perspectives and observations are provided in the conclusion to pave the way for further analyses and innovations. [ABSTRACT FROM AUTHOR]

An approach to identifying the parameters of the motion model for a surface ship subject to external disturbances is proposed. It uses the measurements of heading, yaw rate, speed through the water, and global satellite navigation system (GNSS) data. The model structure is set in the state space. We use the criterion of how close is the real vehicle response to a given control input to its motion model response under the same disturbances. It is proposed to apply the Kalman filter with the state vector including the disturbances, and an iterated procedure for estimating parameters by minimizing the criterion. It is shown that this ensures stable identification of model parameters under different disturbances. Simulation results are presented to evaluate the quality of identification. The approach was validated in full-scale tests of a high-speed boat. [ABSTRACT FROM AUTHOR]

Stand-alone operation of electrical power generators is of increasing interest, because of geographical or financial limitations and reliability issues. This paper presents a simple and innovative approach for sensor-less vector control of stand-alone Doubly Fed Induction generators (DFIG). Unlike grid-connected DFIG, accurate estimation of speed/position of the rotor is crucial for frequency control of stand-alone DFIG. As a new work, an open-loop flux and speed estimator have been developed for the DFIG application. The advantage of a developed speed/angle estimator is its independency of rotor current sensors, the feature that enhances system robustness. Such as other off-line observers, developed estimators are sensitive to machine parameters. To overcome this problem, particle-swarm-optimization has been practically implemented in a digital-signal-processor for parameter identification. Both simulation and experimental results prove the acceptable performance of the proposed approach, especially in preserving the amplitude and frequency of sinusoidal load voltage. [ABSTRACT FROM AUTHOR]

Modular reconfigurable robots (MRRs) have high reconfigurability and can meet the needs of customized production in modern manufacturing. After each MRR is assembled, the reprogramming process frequently requires time-consuming and expensive professional work. The wide applications of MRRs call for automatic reprogramming methods. In this research, an automatic framework is proposed for solving the MRR modeling problem based on the D–H (Denavit–Hartenberg) convention and dynamic parameter identification. Simulations and experiments are conducted on AUBO I-series modular robots. Three other existing modeling algorithms are adopted to make comparison with the proposed one. It is verified that the proposed framework can achieve the desired automatic modeling process with higher accuracy. Specifically, the accuracy of dynamic parameter identification is enhanced by more than 23% in typical experimental scenarios. [ABSTRACT FROM AUTHOR]

In this paper, synchronization analysis and parameters identification issues are explored for uncertain delayed fractional-order BAM neural networks. By designing pertinent state feedback control strategies and parameters updated laws, some ample criteria are procured for ensuring the finite-time synchronization and the Mittag-Leffler synchronization of the considered networks via exploiting the Lyapunov function theory, fractional calculus theory and inequality analysis techniques, meanwhile, the settling time of finite-time synchronization is given, which relates to the initial values. Moreover, parameters identification is actualized triumphantly for uncertain or unknown parameters. Finally, numerical examples are provided to show the availability of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper presents an approach to identify the unknown parameters of genetic regulatory network (GRN) in finite-time. The adaptive synchronization-based method is used to solve this problem. Specifically, an auxiliary system with adaptive parameters is constructed, which is regarded as slave system of the GRN with unknown parameters. Then, effective update laws of adaptive parameters and variable structure controllers are designed. The criteria of synchronization and parameters identification in finite-time are derived by utilizing finite-time Lyapunov stability theorem. Finally, an illustrative example is given to show the effectiveness of the main results. [ABSTRACT FROM AUTHOR]

The problem of identifying the parameters of a dynamic system that describes the oscillations of coupled van der Pol oscillators is considered. A method for constructing a nonlinear identifier is proposed to asymptotically estimate unknown input signals (phase vector of the system) in real time. The method of invariant relations is used to synthesize additional relations between the known and unknown quantities in control problems on the trajectories of the system under consideration. Numerical modeling confirms the applicability of the proposed procedure of solving the identification problem. [ABSTRACT FROM AUTHOR]

Model-based methods are widely used for online states estimation of electric vehicles (EVs) due to its accuracy and robustness. Current research mainly focuses on improving estimation filters. However, there is less discussion on the parameters identification methods. In this work, the parameters identification method is divided into two parts: formulation of the regression model and application of identification algorithms. First, two methods of formulation of the regression model are studied, respectively. Then, performances on parameters identification of forgetting factor recursive least squares (FFRLS), optimal bounding ellipsoid (OBE), and linear Kalman filter (LKF) are discussed. Besides, cubature Kalman filter (CKF) is selected for state of charge (SOC) estimation. In order to obtain the experimental data and verify the parameters identification and states estimation accuracy, the Hybrid Pulse Power Characteristic (HPPC) test, urban dynamometer driving schedule (UDDS) test, and new European driving cycle (NEDC) test are carried out. In addition, the maximum absolute error (MAE), mean absolute error (MaE), and root mean square error (RMSE) are calculated for evaluating the SOC estimation accuracy. When the parameters are identified by LKF, the best performance in MAE, MaE, and RMSE of SOC estimation is obtained. Considering the estimated peak power fluctuation and the identified parameters fluctuation, the OBE algorithm is more suitable for co-estimation of SOC and state of power (SOP). [ABSTRACT FROM AUTHOR]

Background: As a new type of magnetic sensitivity smart material, magnetorheological elastomers (MRE) showing a good magnetorheological effect have a wide application prospect in the field of intelligent structures and devices. Purpose: To accurately characterize the dynamic mechanical behavior of MRE under varying strain amplitudes, frequencies, and magnetic fields, the viscoelastic fractional derivative was introduced, and a phenomenological model with fractional derivative was proposed to depict the hysteresis loops of MRE. Methods: The micro-structure characteristics of MRE with varying mechanical properties were analyzed. The effects of strain amplitudes, frequencies, and magnetic fields on the dynamic viscoelasticity of MRE were studied experimentally. On this basis, the phenomenological model with fractional derivative was established. A parameter identification method was proposed to obtain the prediction model considering the current factor. The parameters of the prediction model were identified by using the GA-PSO optimization algorithm, and the identification results were verified. Results: The storage and loss modulus of MRE first remain unchanged and then decrease with the increasing strain amplitudes (0 ~ 100%); and increase with the ascending frequencies (0 ~ 100 Hz); and increase with the increasing magnetic fields (0 ~ 545 mT), showcasing a significant magnetorheological effect. The numerical results of MRE's stress and strain are in good agreement with the experimental results, and the fitness values exceed 70%, showcasing the effectiveness of the fractional derivative phenomenological model and the feasibility of the parameter identification method. Conclusions: The prediction model can accurately characterize the dynamic mechanical behavior of the MRE under varying strain amplitudes, frequencies, and current intensities, laying a foundation for the engineering application of MRE. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems are becoming increasingly significant because they can convert solar energy into electricity. The conversion efficiency is related to the PV models' parameters, so it is crucial to identify the parameters of PV models. Recently, various metaheuristic methods have been proposed to identify the parameters, but they cannot provide sufficient accurate and reliable performance. To address this problem, this paper proposes a spiral-based chaos chicken swarm optimization algorithm (SCCSO) including three strategies: (1) the information-sharing strategy provides the latest information of the roosters for searching global optimal solution, beneficial to improve the exploitation ability; (2) the spiral motion strategy can enable hens and chicks to move toward their corresponding targets with a spiral trajectory, improving the exploration ability; and (3) a self-adaptive-based chaotic disturbance mechanism is introduced around the global optimal solution to generate a promising solution for the worst chick at each iteration, thereby improving the convergence speed of the chicken flock. Besides, SCCSO is used for identifying different PV models such as the single-diode, the double-diode, and PV module models. Comprehensive analysis and experimental results show that SCCSO provides better robustness and accuracy than other advanced metaheuristic methods. [ABSTRACT FROM AUTHOR]

Hot compression tests of the 6063 aluminum alloy were conducted using a Gleeble-3500 thermal simulation testing machine, and the stress–strain curves at different temperatures and strain rates were obtained. The Kocks–Mecking–Estrin (KME) constitutive model was used to describe the rheological properties, and the initial parameters were identified based on experimental data. The final parameters were identified by the inverse analysis method. The KME model was embedded in a plane compression finite element model by the ABAQUS UHARD subroutine. The multidisciplinary optimization design software ISIGHT was used to integrate the finite element method simulation and relative error calculation. A minimal relative error between the experimental and simulated results was set as the objective, and a multi-island genetic optimization algorithm was used to identify the constitutive parameters. The results showed good agreement between the simulated and measured compression specimen shapes, and the global error between the numerical and measured force–displacement data was only 3.8%. The inverse analysis method was more accurate than the fitting method in identifying the constitutive parameters. The extrusion of a round bar was simulated, and both temperature and extrusion force were accurately predicted by using this constitutive model, further proving that the inverse analysis method used in the present study is effective in identifying the constitutive parameters. [ABSTRACT FROM AUTHOR]

Monitoring systems are common in small and large power plants, providing measurements of basic electrical, mechanical and thermal variables. These measurements can be used for the identification of the synchronous generator parameters. In this paper, a simple method for the identification of a subset of synchronous generator parameters, using basic measurements provided by monitoring systems, is proposed. The identification is performed in normal operating conditions using a synchronous machine simplified model suited for operating conditions under small disturbances. Three identification models are derived, each one depending on a subset of parameters. Three optimization subproblems are then solved by minimizing the residues between each model output and a system measurement. The resulting nonlinear least squares problems are solved by the interior-point method taking into account constraints on the parameters range. A method is proposed to estimate the load angle which is required in the identification. Results are presented for synthetic data and real data, from a 25 MVA generator in a hydroelectric power plant. [ABSTRACT FROM AUTHOR]

State of charge (SOC) is a key parameter of lithium-ion battery. In this paper, a finite difference extended Kalman filter (FDEKF) with Hybrid Pulse Power Characterization (HPPC) parameters identification is proposed to estimate the SOC. The finite difference (FD) algorithm is benefit to compute the partial derivative of nonlinear function, which can reduce the linearization error generated by the extended Kalman filter (EKF) The FDEKF algorithm can reduce the computational load of controller in engineering practice without solving the Jacobian matrix. It is simple of dynamic model of lithium-ion battery to adopt a second-order resistor-capacitor (2RC) network, the parameters of which are identified by the HPPC. Two conditions, both constant current discharge (CCD) and urban dynamometer driving schedule (UDDS), are utilized to validate the FDEKF algorithm. Comparing convergence rate and accuracy between the FDEKF and the EKF algorithm, it can be seen that the former is a better candidate to estimate the SOC. [ABSTRACT FROM AUTHOR]

Accurate estimation of the state of charge (SOC) of a lithium-ion battery is one of the most crucial issues of battery management system (BMS). Existing methods can achieve accurate estimation of the SOC under stable working conditions. However, they may result in inaccuracy under unstable working conditions such as dynamic cycles and different temperature conditions. This is due to the fact that the dynamic behaviors of battery states have not been considered by the parameter identification methods. In this paper, a SOC and parameter joint estimation method is put forward, where the battery model parameters are identified in real time by a particle filter (PF) with consideration of the battery states. Meanwhile, a cubature Kalman filter (CKF) is used to estimate SOC. Then, experiments under dynamic cycles and different temperature conditions are undertaken to assess the performance of the proposed algorithm when compared with the existing joint estimations. The results show that the proposed joint method can achieve a high accuracy and robustness for SOC estimation. [ABSTRACT FROM AUTHOR]

This article is concerned with the identification, from observations or field measurements, of the hydraulic conductivity for the saltwater intrusion problem in an unconfined aquifer. The involved model consists in a cross-diffusion system describing the evolutions of two interfaces: one between freshwater and saltwater and the other one between the saturated and unsaturated zones of the aquifer. The inverse problem is formulated as an optimization problem, where the cost function is a least square functional measuring the discrepancy between experimental interfaces depths and those provided by the model. Considering the exact problem as a constraint for the optimization problem and introducing the Lagrangian associated with the cost function, we prove that the optimality system has at least one solution. Moreover, we establish the first-order necessary optimality conditions. A numerical method is implemented to solve this identification problem. Some numerical results are presented to illustrate the ability of the method to determine the unknown parameters. [ABSTRACT FROM AUTHOR]

This paper investigates on the relationship between the arrangement of collagen fibers within soft tissues and parameters of constitutive models. Starting from numerical experiments based on biaxial loading conditions, the study addresses both the direct (from structure to mechanics) and the inverse (from mechanics to structure) problems, solved introducing optimization problems for model calibration and regression analysis. A campaign of parametric analyses is conducted in order to consider fibers distributions with different main orientation and angular dispersion. Different anisotropic constitutive models are employed, accounting for fibers dispersion either with a generalized structural approach or with an increasing number of strain energy terms. Benchmark data sets, toward which constitutive models are fitted, are built by employing a multiscale description of fiber nonlinearities and accounting for fibers dispersion with an angular integration method. Results show how the optimal values of constitutive parameters obtained from model calibration vary as a function of fibers arrangement and testing protocol. Moreover, the fitting capabilities of constitutive models are discussed. A novel strategy for model calibration is also proposed, in order to obtain a robust accuracy with respect to different loading conditions starting from a low number of mechanical tests. Furthermore, novel results useful for the inverse determination of the mean angle and the variance of fibers distribution are obtained. Therefore, the study contributes: to better design procedures for model calibration; to account for mechanical alterations due to remodeling mechanisms; and to gain structural information in a nondestructive way. [ABSTRACT FROM AUTHOR]

This paper studies parameters identification and minimizing base disturbances problems after the space manipulator capturing an unknown target. A concurrent learning algorithm that concurrently uses past motion data points and instantaneous motion data of the system is proposed for the parameters identification. Given a condition for selecting the used past data points as well as a scaling technique to make the parameters have the same magnitude, the concurrent learning algorithm guarantees that parameters identification errors can globally converge to zero at an exponential rate and without the need for satisfying the persistent excitation (PE) condition. An adaptive reactionless control method is proposed based on the passivity theorem and Task-priority method, which ensures that the base attitude is stationary and joint motions satisfy the limits during the system generating excitation motions for the parameters identification. Simulation results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the “Ocean Rambler” unmanned wave glider (UWG). First, under certain assumptions, the rigid-flexible multi-body system of the UWG was simplified as a rigid system composed of “thruster + float body”, based on which a planar motion model of the UWG was established. Second, we obtained the model parameters using an empirical method combined with parameter identification, which means that some parameters were estimated by the empirical method. In view of the specificity and importance of the heading control, heading model parameters were identified through the artificial fish swarm algorithm based on tank test data, so that we could take full advantage of the limited trial data to factually describe the dynamic characteristics of the system. Based on the established heading motion model, parameters of the heading S-surface controller were optimized using the artificial fish swarm algorithm. Heading motion comparison and maritime control experiments of the “Ocean Rambler” UWG were completed. Tank test results show high precision of heading motion prediction including heading angle and yawing angular velocity. The UWG shows good control performance in tank tests and sea trials. The efficiency of the proposed method is verified. [ABSTRACT FROM AUTHOR]

Soft tissues are characterized by a nonlinear mechanical response, highly affected by the multiscale structure of collagen fibers. The effectiveness and the calibration of constitutive models play a major role on the reliability and the applicability of computational models in biomechanics. This paper presents a procedure for the identification of the relationship between collagen-related structural features in soft tissues with model parameters of classical polynomial- and exponential-based constitutive models. Histological features at microscale, as well as biochemical and biophysical properties at nanoscale, are addressed by employing a multiscale structural description of soft tissue mechanics as benchmark data set. Both the direct (from structure to parameters) and the inverse (from parameters to structure) problem are addressed. Suitable optimization problems are introduced for accurate numerical and approximated analytical direct relationships. The inverse identification has been addressed by providing also a measure of the reliability of the computed estimates. Results show the effectiveness of the proposed strategies and allow to discuss the fitting capabilities of classical constitutive approaches in terms of parameters identification. [ABSTRACT FROM AUTHOR]

Sphere-plane contact is ubiquitous in the fields of equipment manufacturing and intelligent machine design. Research on sphere-plane contact processes not only helps to reveal the causes of instability in terms of contact object motions, functional failures, and vibration noises, but also allows for an accurate prediction of dynamic behavior for the precise control of real-time intelligent machinery. The contact part of the process parameters related to impact are difficult to measure, and current sphere-plane contact impact theory models do not accurately reflect them. Contact process calculations are generally inaccurate, and finite element simulation analysis for the sphere-plane contact impact process is computationally burdensome. In this study, the finite element simulation method was used to obtain contact force, relative penetration, and velocity as input parameter estimations. Contact stiffness, hysteresis damping factors, and elastic impact index were estimated using theoretical model calculations to improve computing velocity and accuracy. [ABSTRACT FROM AUTHOR]

Both environment protection and energy saving have attracted more and more attention in the electric vehicles (EVs) field. In fact, regarding control performance, electric motor has more advantages over conventional internal combustion engine. To decouple the interaction force between vehicle and various coordinating and integrating active control subsystems and estimate the real-time friction force for Advanced Emergency Braking System (AEBS), this paper's primary intention is uniform distribution of longitudinal tire-road friction force and control strategy for a Novel Anti-lock Braking System (Nov- ABS) which is designed to estimate and track not only any tire-road friction force, but the maximum tire-road friction force, based on the Anti-Lock Braking System (ABS). The longitudinal tire-road friction force is computed through real-time measurement of breaking force and angular acceleration of wheels. The Magic Formula Tire Model can be expressed by the reference model. The evolution of the tire-road friction is described by the constrained active-set SQP algorithm with regard to wheel slip, and as a result, it is feasible to identify the key parameters of the Magic Formula Tire Model. Accordingly, Inverse Quadratic Interpolation method is a proper way to estimate the desired wheel slip in regards to the reference of tireroad friction force from the top layer. Then, this paper adapts the Nonlinear Sliding Mode Control method to construct proposed Nov-ABS. According to the simulation results, the objective control strategy turns out to be feasible and satisfactory. [ABSTRACT FROM AUTHOR]

This research work deals with the modeling of the CO adsorption on a commercial adsorbent, based on the temperature dependent Sips and Toth models. In fact, these semi-empirical approaches can properly predict the equilibrium of adsorption of CO, and more specifically in the case of energetically heterogeneous surfaces. In addition, the investigated adsorption models involve several unknown parameters to be estimated from the available adsorption equilibrium experimental data, measured between 303 and 343 K and up to a CO partial pressure of 101 kPa. An estimability analysis was therefore carried out in order to evaluate which parameters are estimable and those that can be fixed either from literature or from previous studies. According to the estimability analysis, whatever the adsorption model which is used, only one over the six unknown parameters is considered as nonestimable. The estimable model parameters are especially the maximum amount adsorbed (q), the equilibrium constant (b), the heterogeneity factors (s and t) assessed at the reference temperature (T) as well as the constant model parameter (α), which is related to the heterogeneity factors (s and t). The estimable parameters were then identified and their optimized values were used in the comparison of the model predictions and the experimental measurements of CO adsorption equilibrium. The results show finally that the model predictions fit well with the experimental data. [ABSTRACT FROM AUTHOR]

Wheeled mobile robots (WMR) are often applied to travel on outdoor unstructured environment, such as loose soil or variable field terrain. Learning the knowledge of terrain has played a significant role for better mobility and stability of WMR. In this study, a diagonal recurrent neural network-based adaptive method is proposed to identify terrain parameters by the platform of a single driving wheel. According to the classical terramechanics model of wheel-soil interaction, a decoupling simplification model is developed by closed-form analytical equations. Five unknown terrain parameters are divided into two groups and included in two complex nonlinear equations. These parameters are used to compute the model outputs of force and torque of wheel-soil interaction. Dynamic back propagation algorithm is applied to update these parameters for compensating the errors between the prediction of neural network and measurable data in real time. The results of simulation show that the terrain parameters can be obtained and approximate the experimental value of terrain parameters when the predictive errors converge to zero. [ABSTRACT FROM AUTHOR]

A method for the recursive identification of physiological models of the cardiovascular baroreflex is proposed and applied to the time-varying analysis of vagal and sympathetic activities. The proposed method was evaluated with data from five newborn lambs, which were acquired during injection of vasodilator and vasoconstrictors and the results show a close match between experimental and simulated signals. The model-based estimation of vagal and sympathetic contributions were consistent with physiological knowledge and the obtained estimators of vagal and sympathetic activities were compared to traditional markers associated with baroreflex sensitivity. High correlations were observed between traditional markers and model-based indices. [ABSTRACT FROM AUTHOR]

This paper deals with the permanent magnet synchronous motor (PMSM) speed estimation and parameters identification problems. It proposes a solution to estimate speed and identify stator resistance, d-axis and q-axis inductance simultaneously. In fact, there are a lot of methods for estimating the speed, for example high-frequency injection, Kalman filter, and model reference adaptive system (MRAS). However, with the PMSM running, some parameters will change such as the stator resistance will change with the increase in temperature. These parameters are useful for estimating the speed. So, it is necessary to identify parameters simultaneously to adapt to the changes of these parameters. In this paper, MRAS is the theoretical basis. First, the adaptive law of speed observer is constructed through Popov stability. Then, the adaptive laws of stator resistance, d-axis, and q-axis inductance are constructed through Lyapunov stability. The stability of the proposed observers is also discussed. Last, this paper uses field-oriented control strategy and illustrates the obtained results through simulation and experiment. [ABSTRACT FROM AUTHOR]

In this paper, the exponential and finite-time synchronization and parameters identification for uncertain multi-links complex network are investigated by using the impulsive control method. It is known that impulsive control, as an effective and ideal control technique, can not only realize the synchronization goal but also reduce the control cost. The paper proposes an impulsive complex network model with uncertain parameter. Two different kinds of adaptive feedback controllers are further designed based on impulsive delay differential inequalities, and the purpose is to achieve the exponential synchronization, finite-time synchronization and obtain parameters identification simultaneously. Several novel and useful exponential and finite-time synchronization criteria are also derived based on global exponential stability theory and finite-time stability theory. Finally, two numerical simulations are provided to illustrate the effectiveness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

The static characteristics of a taper joint interface play a key role in the static behavior of the entire spindle system of a machine tool. In this study, a method of modeling a taper joint and identifying its parameters is proposed to describe the static behavior of a taper joint. First, a static model is constructed based on a virtual material layer and equilibrium functions established to identify the unknown parameters of the virtual material. Then, an experimental method is introduced to acquire joint interface data for parameter identification based on a designed spindle-holder characteristic test system. In particular, a BT40 taper joint holder is studied taking into account the pre-force at the end of the holder. The study results show that the static stiffness increased initially with the growth in the pre-force and became relatively stable after reaching a value which could be explained by its microstructure. According to the simulation results, the effectiveness of the proposed virtual material modeling method, compared with previous methods, are discussed. The estimated parameters are informative for the design and manufacture of the spindle. [ABSTRACT FROM AUTHOR]

An inverse analysis methodology for determining the parameters of the kinematic law of sheet metals is proposed. The sensitivity of the load versus displacement curves, obtained by reverse shear tests of rectangular and notched specimens, to the kinematic law parameters are studied following a forward analysis, based on finite element simulations. Afterwards, an inverse analysis methodology using a gradient-based Levenberg-Marquardt method is established, by evaluating the relative difference between numerical and experimental results of the shear test, i.e. the load evolution in function of the displacements of the grips. The use of a notched specimen is proposed in order to allow an easy and suitable numerical representation of the boundary conditions of the shear experimental test. This methodology has proven to be appropriate for determining the parameters of the kinematic hardening law. [ABSTRACT FROM AUTHOR]

Monitoring of the biological degradation of a substrate by microorganisms is a key issue, especially in the soil bioremediation area. Respiration measurement is the easiest way to obtain online information on the biological activity. Nevertheless, it is indirectly related to substrate consumption and microbial growth. To be able to link these phenomena, a robust and descriptive model has been developed. Both biological and gas/liquid transfer dynamics must be taken into account to link the online measurement with the actual biological respiration. For that, experimental evolution of the respiratory ouotient (RQ) during a biodegradation has been compared against general biodegradation knowledge. To obtain a reliable model, practical and structural sensitivity analyses have been conducted. The model can describe the evolution of both online measurable and non-measurable states. It also gives a new definition of the apparent RQ, measured in the gas phase, compared to the actual biological RQ. [ABSTRACT FROM AUTHOR]

Fluid viscous dampers are extensively adopted as efficient and cheap energy dissipation devices in structural seismic protection. If we consider the usefulness of these passive control devices, the exact recognition of their mechanical behavior is of outstanding importance to provide a reliable support to design a very efficient protection strategy. In scientific and technical applications, many different constitutive models have been proposed and adopted till now to represent fluid viscous dampers, with different levels of complexity and accuracy. This paper focuses on parameter identification of fluid viscous dampers, comparing different existing literature models, with the aim to recognize the ability of these models to match experimental loops under different test specimens. The identification scheme is developed evaluating the experimental and the analytical values of the forces experienced by the device under investigation. The experimental force is recorded during the dynamic test, while the analytical one is obtained by applying a displacement time history to the candidate mechanical law. The identification procedure furnishes the device mechanical parameters by minimizing a suitable objective function, which represents a measure of the difference between the analytical and experimental forces. To solve the optimization problem, the particle swarm optimization is adopted, and the results obtained under various test conditions are shown. Some considerations about the agreement of different models with experimental data are furnished, and the sensitivity of identified parameters of analyzed models against the frequency excitation is evaluated and discussed. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid differential evolution (HDE) algorithm-based parameter identification approach is proposed in terms of the voltage-current characteristics for the problem of proton exchange membrane (PEM) fuel cell stack modeling. It considers the convergence speed and the computational cost simultaneously by non-periodic partial increasing or declining individuals according to fitness diversities. Thus, this active control approach realizes the identification of the SR-12 Modular PEM Generator, the Ballard Mark V FC, and the BCS 500-W stack in both deterministic and stochastic environments, respectively. Calculation results reveal that the proposed HDE is broader in applicability than other six State-of-the-Art algorithms. [ABSTRACT FROM AUTHOR]

For the problem of image registration (binding), we examine whether it is possible to increase the prediction accuracy of binding parameters between two centers of local image fragments for which binding parameters have been determined. We study how to use linear and cubic polynomials, a Bezier curve, a bi-arc curve, and a rational Bezier curve for this purpose. [ABSTRACT FROM AUTHOR]

When analyzing the dynamics of the hard-coating composite structure, it is necessary to determine the mechanical parameters (including Young's modulus and loss factor) of the coating material through experiment. We derived an identification formula for extracting the mechanical parameters of small damping materials with consideration of the loss factor of metal substrate. Based on the identification formula, a practical methodology and relative test procedure for estimating mechanical parameters of hard-coating materials with strain-dependence were proposed. Then, a common hard-coating material NiCrAlY was taken as study case and its strain-dependence Young's modulus and loss factor were identified. The obtained mechanical parameters were inputted into the finite element model, the rationale was verified by a comparison of the finite element modal analysis and the experiments. [ABSTRACT FROM AUTHOR]

An assistive robot is a novel service robot, playing an important role in the society. For instance, it can amplify human power not only for the elderly and disabled to recover/rehabilitate their lost/impaired musculoskeletal functions but also for healthy people to perform tasks requiring large forces. Consequently, it is required to consider both accurate position control and human safety, which is the compliance. This paper deals with the robot control compliance problem based on the QNX real-time operating system. Firstly, the mechanical structure of a compliant joint on the assistive robot is designed using Solidworks. Then the parameters of the assistive robot system are identified. The software of robot control includes data acquisition and processing, and control to meet the compliance requirement of the joint control. Finally, a Hogan impedance control experiment is carried out. The experimental results prove the effectiveness of the method proposed. [ABSTRACT FROM AUTHOR]

In the preliminary design stage of high-speed train smart suspension, a simple, yet accurate magnetorheological (MR) damper model whose parameters have clear physical meaning is needed. Based on the working mechanism analysis and the dynamic behavior study of the MR damper, a new consecutive viscoelastic plastics (VEP) model is proposed. A methodology to find the parameters of the proposed model directly has been proposed. The comparison with experimental results indicates that the proposed model could adequately characterize the intrinsic nonlinear behavior of the MR damper, including the hysteretic behavior, roll-off phenomenon, and the variation of the hysteresis width in terms of the frequency and magnitude of excitation. The results of experimental testing prove that the accuracy of the proposed model is higher than that of the phenomenological model while only containing four undetermined parameters with clear physical meaning. Moreover, based on the proposed VEP model, a nonlinear stiffness VEP (nkVEP) model is developed with higher precision in the hysteretic region. The nkVEP model, which can reproduce the behavior of the damper with fluctuating input current, is developed. The proposed model could predict accurately the response of the MR damper in a wide range of frequency and displacement. [ABSTRACT FROM AUTHOR]

Dynamic behavior of spindle-holder taper joint is critical in the spindle system of machine tool, related to the machining process. The parameters of the holder joint are significant for the spindle-holder system. This paper developed a new approach to investigate the dynamic behavior of the taper joint in modeling and parameters identification. Firstly, dynamic model is illustrated in both radial and axial directions based on the pairs of spring and damping. And frequency response function is used to identify the unknown modal parameters of frequencies and damping ratios. Secondly, a platform is designed and manufactured to establish the testing system in order to investigate the taper joint. Furthermore, taper joint of a BT50 holder is investigated, and the pre-force at the end of holder is the main consideration during the testing. The results show that the dynamic stiffness increases with the increasing of pre-force at the end of the holder. In addition, the paper discusses the phenomenon in microstructure based on peak-hollows model and the joint parameters are proved to be workable through simulation. The joint parameters are instructive to the design and manufacturing of the spindle. [ABSTRACT FROM AUTHOR]

Determination of geomechanical parameters of petroleum reservoir and surrounding rock is important for coupled reservoir-geomechanical modeling, borehole stability analysis and hydraulic fracturing design. A displacement back analysis technique based on artificial neural network (ANN) and genetic algorithm (GA) combination is investigated in this paper to identify reservoir geomechanical parameters based on ground surface displacements. An ANN is used to map the nonlinear relationship between Young's modulus, E, Poisson's ratio, v, internal friction angle, Φ, cohesion, c and ground surface displacements. The necessary training and testing samples for ANN are created by using numerical analysis. GA is used to search the set of unknown reservoir geomechanical parameters. Results of the numerical experiment show that the displacement back analysis technique based on ANN-GA combination can effectively identify reservoir geomechanical parameters based on ground surface movements as a result of oil and gas production. [ABSTRACT FROM AUTHOR]

With the improvement of the aircraft flight performance and development of computing science, uninhabited combat aerial vehicle (UCAV) could accomplish more complex tasks. But this also put forward stricter requirements for the flight control system, which are the crucial issues of the whole UCAV system design. This paper proposes a novel UCAV flight controller parameters identification method, which is based on predator-prey particle swarm optimization (PSO) algorithm. A series of comparative experimental results verify the feasibility and effectiveness of our proposed approach in this paper, and a predator-prey PSO-based software platform for UCAV controller design is also developed. [ABSTRACT FROM AUTHOR]

This paper discusses how to identify tire model coefficients that are used to compare longitudinal forces when braking. Those in the automotive world have worked extremely hard to obtain these parameters and different methods have been used to match the values of these parameters. This paper proposes the use of self-organizing maps to tackle this problem whereby interactively searches are carried out to find the optimum tire model parameters. The objective of this research is to prove the capability of self-organizing maps (SOMs) to classify a vehicle's braking formula on a roller brake tester from the MOT (Ministry of Transport) and on flat ground. The neural network produced a good brake-slip ratio when presented with data that are not used in network training. This means that the methodology is feasible. This tool easily obtains the brake-slip equation of each experiment and the braking on two different experimental tests will be compared. [ABSTRACT FROM AUTHOR]

The purpose of this study was to effectively identify parameters for a LuGre friction model based on experimental measures. In earlier work related to this study (Yang et al., 2009), which was based on the characters of polygonal wear (Sueoka and Ryu, 1997), we showed a frictional vibration model for a mass on a moving belt. This model reflected lateral vibrations caused by velocity and toe-in angle. An important aspect of the present study is the improved friction model. A previous friction model, which divided the process into four parts, expressed the sable excited vibration well but failed to reflect the hysteresis loop change when vehicles accelerated or decelerated continuously. A LuGre friction model can solve this problem, but several model parameters must be obtained experimentally. We measured contact width and length of tires as vertical pressure changed; this provided a theoretical basis for apparent stiffness of a unit of tire tread. Based on tire data from Bakker E's article in a SAE paper from 1987, we identified the Stribeck exponent and Stribeck velocity in LuGre. Then, the results were implemented in a vibration system that verified the rationality of the data. [ABSTRACT FROM AUTHOR]

In this article, we describe an approach to model the electromechanical behavior of the skeletal muscle based on the Huxley formulation. We propose a model that complies with a well established macroscopic behavior of striated muscles where force-length, force-velocity, and Mirsky-Parmley properties are taken into account. These properties are introduced at the microscopic scale and related to a tentative explanation of the phenomena. The method used integrates behavior ranging from the microscopic to the macroscopic scale, and allows the computation of the dynamics of the output force and stiffness controlled by EMG or stimulation parameters. The model can thus be used to simulate and carry out research to develop control strategies using electrical stimulation in the context of rehabilitation. Finally, through animal experiments, we estimated model parameters using a Sigma Point Kalman Filtering technique and dedicated experimental protocols in isometric conditions and demonstrated that the model can accurately simulate individual variations and thus take into account subject dependent behavior. [ABSTRACT FROM AUTHOR]

Systems biology aims at building computational models of biological pathways in order to study in silico their behaviour and to verify biological hypotheses. Modelling can become a new powerful method in molecular biology, if correctly used. Here we present step-by-step the derivation and identification of the dynamical model of a biological pathway using a novel synthetic network recently constructed in the yeast Saccharomyces cerevisiae for In-vivo Reverse-Engineering and Modelling Assessment. This network consists of five genes regulating each other transcription. Moreover, it includes one protein-protein interaction, and its genes can be switched on by addition of galactose to the medium. In order to describe the network dynamics, we adopted a deterministic modelling approach based on non-linear differential equations. We show how, through iteration between experiments and modelling, it is possible to derive a semi-quantitative prediction of network behaviour and to better understand the biology of the pathway of interest. [ABSTRACT FROM AUTHOR]

novel maglev transportation system was proposed for large travel range ultra precision motion. The system consists of a levitation subsystem and a propulsion subsystem. During the propulsion subsystem driving the moving platform along the guideway, the levitation subsystem uses six pairs of electromagnets to steadily suspend the moving platform over the guideway. The model of the levitation system, which is a typical nonlinear multi-input multi-output coupling system and has many inner nonlinear coupling characteristics, was deduced. For testifying the model, the levitation mechanism was firstly controlled by proportional-integral-differential (PID) control, and then a lot of input-output data were collected for model parameter identification. The least-square parameter identification method was used. The identification results prove that the model is feasible and suitable for the real system. [ABSTRACT FROM AUTHOR]

This paper investigates projectively lag synchronization of a new hyperchaotic system (proposed by Yang et al. in Nonlinear Anal., Real World Appl., 10:1601, ), with certain/uncertain parameters. Based on the Lyapunov function constructing method and application of the Babarǎt lemma, projectively lag synchronization is achieved for the new hyperchaotic system by designing nonlinear controllers. Furthermore, a novel method is proposed to identify unknown parameters based on projectively lag synchronization of the new hyperchaotic system. Finally, several numerical simulations are given to verify and test the correctness and effectiveness of the novel methods we proposed. [ABSTRACT FROM AUTHOR]

The modern systems biology approach to the study of molecular cellular biology, consists in the development of computational tools to support the formulation of new hypotheses on the molecular mechanisms underlying the observed cell behavior. Recent biotechnologies are able to provide precise measures of gene expression time courses in response to a large variety of internal and environmental perturbations. In this paper, we propose a simple algorithm for the selection of the “best” regulatory network motif among a number of alternatives, using the expression time course of the genes which are the final targets of the activated signalling pathway. To this aim, we considered the Hill nonlinear ODEs model to simulate the behavior of two ubiquitous motifs: the single input motif and the multi output feed-forward loop motif. Our algorithm has been tested on simulated noisy data assuming the presence of a step-wise regulatory signal. The results clearly show that our method is potentially able to robustly discriminate between alternative motifs, thus providing a useful in silico identification tool for the experimenter. [ABSTRACT FROM AUTHOR]

This work deals with the identification of constitutive parameters by inverse methodology. Two different approaches are presented and analysed: the single-point and FE analysis. The use of these two different methodologies for the evaluation of objective functions in the identification process is still an open question and the interest in this field has been increasing among the metal forming community. To discuss this issue, two different constitutive models suitable for metals were used, i.e. an elastoplastic hardening model and an elastoplastic model with isotropic and kinematic hardening. The determined material parameters for the two models, the respective objective function values and the CPU time required to perform the simulations are presented and discussed. [ABSTRACT FROM AUTHOR]

Developments in the numerical simulation of the hydroforming process of tubular metallic components, tailor-welded before forming, are presented. Both technologies, tailor-welded joining operations and hydroforming processes, are well known in industry, although most commonly separately used. Tailor-welded joining operations are usually encountered in plain sheets, subsequently formed by stamping. Tube hydroforming processing, on the other hand, is frequently associated with parts consisting of uniform thickness, material properties, and dimensions. The present analysis focuses on the influence of process parameters, such as the position of the weld-line and initial thickness values, in the innovative process of combining tailor-welded tubes (with distinct thickness values) and hydroforming. Particular attention is posed on the relation of imposed axial displacement vs. imposed hydraulic pressure into the tube, forming parameters that are not known a-priori in the manufacturing of a new part. Another point of practical interest is the numerical simulation of the weld-line movement, after forming is complete. The finite element method is directly employed in the numerical simulation by means of innovative solid elements suited for incompressibility applications, and included by the authors into the commercial program ABAQUS as user-elements. The obtained results can then lead to a better understanding, along with design tools, for the process of hydroforming of tailor-welded tubular parts, accounting for dissimilar thickness of the basic components. [ABSTRACT FROM AUTHOR]