Search

Objective: This study was focused on the estimation of parameters of Wood’s model and description of the lactation curve using the cows which were lactated over 24 months on the first lactation. Methods: The database included 1,333 pure-bred dairy Simmental primiparous cows which lactated for 24 months (732 days). The initial dataset entering the procedure of assessment of parameters of Wood’s function included 35,826 milk yield records. Milk yield was recorded throughout lactation, with the earliest record taken on day 6 and the latest on day 1,348 of lactation. This dataset was used for the assessment of parameters a, b, c of Wood’s model using the non-linear statistical procedure. These parameters were estimated for different length of lactation. The assessed parameters were used for calculation of some characteristics of lactation curves. Results: The lowest value of a parameter (15.2317) of Wood’s model of lactation curve was found out in lactations up to 305 days long, contrary to b and c parameters which were highest in those lactations (0.1029 and 0.0015, respectively). The maximum value of a parameter (17.4329) was found out in lactations up to 640 days long, unlike b and c parameters which were minimal in those lactations (0.0603 and 0.0010, respectively). Conclusion: It can be concluded that the parameters of Wood’s model and the shape of lactation curve are changing with the growing number of milk yield records. Also, the assessed parameters revealed a significant milk production potential after 305 days of lactation. [ABSTRACT FROM AUTHOR]

This paper presents a numerical procedure based on a distribution approach for doing parameter estimation in nonlinear dynamical models. The originality of the paper is first, to present a complete study of the errors due to the method and due to the noise on the signal then, to apply it to a recent model describing the transmission of the chikungunya virus to the human population. The advantage of this numerical procedure is not to require any knowledge about the value of the parameters or about the statistics of measurement uncertainties. Furthermore, it attenuates a part of the noise improving consequently the results of the parameter estimation. The numerical results attest the relevance of this approach. [ABSTRACT FROM AUTHOR]

Most of the technological innovation diffusion follows an S-shaped curve. But, in many practical situations this may not hold true. To this end, Weibull model was proposed to capture the diffusion of new technological innovation, which does not follow any specific pattern. Nonlinear growth models play a very important role in getting an insight into the underlying mechanism. These models are generally 'mechanistic' as the parameters have meaningful interpretation. The nonlinear method of estimation of parameters of Weibull model fails to converge. Taking this problem into consideration, we propose the use of a powerful technique of genetic algorithm for parameter estimation. The methodology is also validated by simulation study to check whether parameter estimates are closer to the real value. For illustration purpose, we model the tractor density time-series data of India as a whole and some major states of India. It is seen that fitted Weibull model is able to capture the technology diffusion process in a reasonable manner. Further, comparison is also made with Logistic and Gompertz model; and is found to perform better for the data sets under consideration. [ABSTRACT FROM AUTHOR]

This study investigates the parameter estimation with general quantised inputs and binary-valued output observations for systems with structural uncertainties of the time-dependent bias and the non-linear model mismatch. Combining the empirical-measure-based technique and the least-squares optimisation, an identification algorithm is proposed by use of the output threshold information and the distribution function of the system noise. It is shown that the estimate of the unknown parameter can be sandwiched in between two constructed auxiliary sequences, whose limits are just a lower bound of the limit inferior of the algorithm and an upper bound of the limit superior, respectively. Moreover, probabilistic estimation error bounds are given. Numerical simulations are presented to illustrate the main theoretical results. [ABSTRACT FROM AUTHOR]

The article studies the log-logistic class of dose-response bioassay models in the binomial set-up. The dose is identified by the potency adjusted mixing proportions of two similar compounds. Models for both absence and presence of interaction between the compounds have been considered. The aim is to investigate the D- and Ds-optimal mixture designs for the estimation of the full set of parameters or for the estimation of potency for a best guess of the parameter values. We also indicate how to find the optimal design to estimate the mixing proportions at which the probability of success attains a given value in the absence of the interaction effect. [ABSTRACT FROM AUTHOR]

Serious effects of noise present in room impulse responses (RIRs) on evaluation of room acoustical parameters can be compensated by applying different methods. One of the preferred methods is based on the noise subtraction, RIR truncation and correction for truncation. This paper presents a procedure for calculation of the correction term that should be added to the backward integrated truncated RIR. The procedure can be applied in an automated way, and it is based on a nonlinear decay model consisting of an exponential decay plus stationary noise. Unknown parameters of the model are calculated by an optimization procedure where the model is fitted to the measurements. The proposed procedure for correction term calculation is tested on both synthesized and measured RIRs. The effects of the procedure parameters related to the RIR estimation range used for fitting the model to the measurements are analyzed. This procedure is compared with the one often applied in practice as a reference. The comparison shows that the proposed procedure outperforms the reference one. In addition, the impact of changing the correction term on the reverberation time estimation is investigated. [ABSTRACT FROM AUTHOR]

In nonlinear regression analysis, the residual sum of squares may possess multiple local minima. This complicates finding the global minimum and adversely affects reliability of the relevant statistical methods. Identifying and sizing up the regions of a readily identifiable global minimum (RIGM) is therefore of both theo- retical and practical interest. This paper addresses the issue by using equidistant function previously introduced by the first two co-authors of this paper. [ABSTRACT FROM AUTHOR]

Response surfaces are common surrogates for expensive computer simulations in engineering analysis. However, the cost of fitting an accurate response surface increases exponentially as the number of model inputs increases, which leaves response surface construction intractable for high-dimensional, nonlinear models. We describe ridge approximation for fitting response surfaces in several variables. A ridge function is constant along several directions in its domain, so fitting occurs on the coordinates of a low-dimensional subspace of the input space. We review essential theory for ridge approximation – e.g., the best mean-squared approximation and an optimal low-dimensional subspace – and we prove that the gradient-based active subspace is near-stationary for the least-squares problem that defines an optimal subspace. Motivated by the theory, we propose a computational heuristic that uses an estimated active subspace as an initial guess for a ridge approximation fitting problem. We show a simple example where the heuristic fails, which reveals a type of function for which the proposed approach is inappropriate. We then propose a simple alternating heuristic for fitting a ridge function, and we demonstrate the effectiveness of the active subspace initial guess applied to an airfoil model of drag as a function of its 18 shape parameters. [ABSTRACT FROM AUTHOR]

The structure of the optimization procedure may affect the control quality of nonlinear model predictive control (MPC). In this paper, a data-driven optimization framework for nonlinear MPC is proposed, where the linguistic model is employed as the prediction model. The linguistic model consists of a series of fuzzy rules, whose antecedents are the membership functions of the input variables and the consequents are the predicted output represented by linear combinations of the input variables. The linear properties of the consequents lead to a quadratic optimization framework without online linearisation, which has analytical solution in the calculation of control sequence. Both the parameters in the antecedents and the consequents are calculated by a hybrid-learning algorithm based on plant data, and the data-driven determination of the parameters leads to an optimization framework with optimized controller parameters, which could provide higher control accuracy. Experiments are conducted in the process control of biochemical continuous sterilization, and the performance of the proposed method is compared with those of the methods of MPC based on linear model, the nonlinear MPC with neural network approximator, and MPC nonlinear with successive linearisations. The experimental results verify that the proposed framework could achieve higher control accuracy. [ABSTRACT FROM AUTHOR]

This article revisits recently proposed methods to determine the kernel parameter and the number of latent components for identifying kernel principal component analysis (KPCA) and kernel partial least squares (KPLS) models. A detailed analysis shows that existing work is neither optimal nor efficient in determining these important parameters and may lead to erroneous estimates. In addition to that, most methods are not designed to simultaneously estimate both parameters, i.e. they require one parameter to be predetermined. To address these practically important issues, the article introduces a cross-validatory framework to optimally determine both parameters. Application studies to a simulation example and a total of three experimental or industrial data sets confirm that the cross-validatory framework outperforms existing methods and yields optimal estimations for both parameters. In sharp contrast, existing work has the potential to substantially overestimate the number of latent components and to provide inadequate estimates for the kernel parameter. [ABSTRACT FROM AUTHOR]

The analysis of human perceptions is often carried out by resorting to surveys and questionnaires, where respondents are asked to express ratings about the objects being evaluated. A class of mixture models, called CUB (Combination of Uniform and shifted Binomial), has been recently proposed in this context. This article focuses on a model of this class, the Nonlinear CUB, and investigates some computational issues concerning parameter estimation, which is performed by Maximum Likelihood. More specifically, we consider two main approaches to optimize the log-likelihood: the classical numerical methods of optimization and the EM algorithm. The classical numerical methods comprise the widely used algorithms Nelder–Mead, Newton–Raphson, Broyden–Fletcher–Goldfarb–Shanno (BFGS), Berndt–Hall–Hall–Hausman (BHHH), Simulated Annealing, Conjugate Gradients and usually have the advantage of a fast convergence. On the other hand, the EM algorithm deserves consideration for some optimality properties in the case of mixture models, but it is slower. This article has a twofold aim: first we show how to obtain explicit formulas for the implementation of the EM algorithm in nonlinear CUB models and we formally derive the asymptotic variance–covariance matrix of the Maximum Likelihood estimator; second, we discuss and compare the performance of the two above mentioned approaches to the log-likelihood maximization. [ABSTRACT FROM AUTHOR]

This paper studies recursive composite hypothesis testing in a network of sparsely connected agents. The network objective is to test a simple null hypothesis against a composite alternative concerning the state of the field, modeled as a vector of (continuous) unknown parameters determining the parametric family of probability measures induced on the agents’ observation spaces under the hypotheses. Specifically, under the alternative hypothesis, each agent sequentially observes an independent and identically distributed time-series consisting of a (nonlinear) function of the true but unknown parameter corrupted by Gaussian noise, whereas, under the null, they obtain noise only. Two distributed recursive generalized likelihood ratio test type algorithms of the consensus+innovations form are proposed, namely, \mathcal CIGLRT-L and \mathcal CIGLRT-NL , in which the agents estimate the underlying parameter and in parallel also update their test decision statistics by simultaneously processing the latest local sensed information and information obtained from neighboring agents. For \mathcal CIGLRT-NL , for a broad class of nonlinear observation models and under a global observability condition, algorithm parameters which ensure asymptotically decaying probabilities of errors (probability of miss and probability of false detection) are characterized. For \mathcal CIGLRT-L , a linear observation model is considered and upper bounds on large deviations decay exponent for the error probabilities are obtained. [ABSTRACT FROM AUTHOR]

In recent years there has been an increased interest in using statistical models for analysis of single dengue outbreaks based on the reported cumulative cases. Sometimes this type of data is collected for all urban areas in a particular region. Modeling in order to estimate epidemiological parameters is usually performed for each area separately, but when the interest lays on estimating the average behavior of a particular area in the population, and variability among and within areas, a nonlinear mixed effects model is recommended. In this research, we describe two approaches that provide estimates of three key epidemiological parameters: the turning point, the final size of outbreak, and the basic reproduction number R0, using nonlinear models. The first approach consists of fitting an individual nonlinear model for each area separately. In the second method we use a nonlinear mixed effects model, which accounts for heterogeneity between areas. In both approaches, the Richards model was used as mean structure. The proposed methods are applied to data of seven Primary-Health Care Areas of Plaza municipality, Havana City, Cuba during 2006 dengue outbreak. [ABSTRACT FROM AUTHOR]

A nonlinear model for fatigue damage accumulation under variable amplitude loading is presented. The known assumption that the isodamage curves are converging at the knee point of the S-N curve of the material, has been adopted. The proposed model does only require one parameter, in addition to the S-N curve of the material, in order to calculate the remaining fatigue life. A single value for the parameter has been found to give satisfying agreement with experimental data for four arbitrary selected steels, indicating a general trend. [ABSTRACT FROM AUTHOR]

While the theory of stability for nonlinear model predictive control (NMPC) has been under extensive study, partial stability is apparently overlooked. In this paper we apply the concept of partial stability in order to extend several results for the stability analysis of NMPC and investigate the behavior of NMPC for dynamic systems with uncertain parameters. Partial stability for NMPC is established without using terminal costs and terminal constraints. Numerical examples are provided to illustrate the obtained results. [ABSTRACT FROM AUTHOR]

This paper addresses the important topic of electro-mechanical systems identification with an application in robotics. The standard inverse dynamic identification model with least squares (IDIM-LS) method of identifying models for robotic systems is based on the use of a continuous-time inverse dynamic model whose parameters are identified from experimental data by linear LS estimation. The paper describes a new alternative but related approach that exploits the state-dependent parameter (SDP) method of nonlinear model estimation and compares its performance with that of IDIM-LS. The SDP method is a two-stage identification procedure able to identify the presence and graphical shape of nonlinearities in dynamic system models with a minimum of a priori assumptions. The performance of the SDP method is evaluated on two electro-mechanical systems: the electro-mechanical positioning system and the second link of the TX40 robot. The experimental results demonstrate how SDP identification helps to avoid over-reliance on prior conceptions about the nature of the nonlinear characteristics and correct any deficiencies in this regard. Finally, a simulation study shows how the resulting SDP model is able to facilitate nonlinear control system design using linear-like design procedures. [ABSTRACT FROM AUTHOR]

An output-feedback approach to model predictive control that combines state estimation and control into a single min–max optimization is introduced for discrete-time nonlinear systems. Specifically, a criterion that involves finite forward and backward horizons is minimized with respect to control input variables and is maximized with respect to the unknown initial state as well as disturbance and measurement noise variables. Under appropriate assumptions that encode controllability and observability, we show that the state of the closed-loop remains bounded and that a bound on tracking error can be found for trajectory-tracking problems. We also introduce a primal–dual interior-point method that can be used to efficiently solve the min–max optimization problem and show in simulation examples that the method succeeds even for severely nonlinear and non-convex problems. [ABSTRACT FROM AUTHOR]

In this study, an enhanced Kalman Filter formulation for linear in the parameters models with inherent correlated errors is proposed to build up a new framework for nonlinear rational model parameter estimation. The mechanism of linear Kalman filter (LKF) with point data processing is adopted to develop a new recursive algorithm. The novelty of the enhanced linear Kalman filter (EnLKF in short and distinguished from extended Kalman filter (EKF)) is that it is not formulated from the routes of extended Kalman Filters (to approximate nonlinear models by linear approximation around operating points through Taylor expansion) and also it includes LKF as its subset while linear models have no correlated errors in regressor terms. No matter linear or nonlinear models in representing a system from measured data, it is very common to have correlated errors between measurement noise and regression terms, the EnLKF provides a general solution for unbiased model parameter estimation without extra cost to convert model structure. The associated convergence is analysed to provide a quantitative indicator for applications and reference for further research. Three simulated examples are selected to bench-test the performance of the algorithm. In addition, the style of conducting numerical simulation studies provides a user-friendly step by step procedure for the readers/users with interest in their ad hoc applications. It should be noted that this approach is fundamentally different from those using linearisation to approximate nonlinear models and then conduct state/parameter estimate. [ABSTRACT FROM PUBLISHER]

ABSTRACT We propose in this study an empirical nonlinear model to extract DC intrinsic transconductance ( gmDC) and output conductance ( gdsDC) from the high frequency intrinsic parameter set. The proposed method based on a nonlinear equation show a very good agreement in DC I- V characteristics between the calculations and measurements with a fitting error of 0.25% and enables us successfully extract the gmDC and gdsDC. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 59:131-134, 2017 [ABSTRACT FROM AUTHOR]

This paper presents a technique to perform pose estimation of planar three degrees of freedom (DOF) motions using two-plate capacitive sensors. These sensors are commonly utilised to measure linear motion, however their capacitance response becomes nonlinear and errors can be introduced with the introduction of angular motions. This paper focusses on the development of a methodology to invert a nonlinear model which maps the pose of the end effector of a planar positioner to the capacitances of each sensor, in effect providing pose estimates from capacitance measurements. Strategies for the calibration of model parameters, for cases where full reference or only yaw sensor measurements are available, are extensively explored. The points evaluated through the proposed method are shown to outperform the best affine transformation when trained with a full reference, and improved performance is also observed when the single reference is employed. [ABSTRACT FROM AUTHOR]

Potential assessment has served various objectives in the development of renewable energies. However, the prospective nature of this type of assessment sometimes makes it difficult to evaluate and compare estimation results based on different data and modeling. To facilitate this comparison, uncertainty estimates need to be systematically provided. Since potential assessment sometimes relies on numerous parameters, this may first require determining the most important inputs to focus on. In this paper, we propose a sensitivity analysis methodology based on Sobol indices so as to identify the main inputs from a nonlinear assessment model. We illustrate the proposed methodology through analyzing sensitivity in an onshore wind and ground solar photovoltaic (PV) potential assessment covering two French regions. As a result, we show that, when estimating the potential of these renewable energy sources, parameters defining surface availability are more prevalent than those related to technology. [ABSTRACT FROM AUTHOR]

A powerful way of gaining insight into biological systems is by creating a nonlinear differential equation model, which usually contains many unknown parameters. Such a model is called structurally identifiable if it is possible to determine the values of its parameters from measurements of the model outputs. Structural identifiability is a prerequisite for parameter estimation, and should be assessed before exploiting a model. However, this analysis is seldom performed due to the high computational cost involved in the necessary symbolic calculations, which quickly becomes prohibitive as the problem size increases. In this paper we show how to analyse the structural identifiability of a very general class of nonlinear models by extending methods originally developed for studying observability. We present results about models whose identifiability had not been previously determined, report unidentifiabilities that had not been found before, and show how to modify those unidentifiable models to make them identifiable. This method helps prevent problems caused by lack of identifiability analysis, which can compromise the success of tasks such as experiment design, parameter estimation, and model-based optimization. The procedure is called STRIKE-GOLDD (STRuctural Identifiability taKen as Extended-Generalized Observability with Lie Derivatives and Decomposition), and it is implemented in a MATLAB toolbox which is available as open source software. The broad applicability of this approach facilitates the analysis of the increasingly complex models used in systems biology and other areas. [ABSTRACT FROM AUTHOR]

In this paper we introduce a weighted composite quantile regression (CQR) estimation approach and study its application in nonlinear models such as exponential models and ARCH-type models. The weighted CQR is augmented by using a data-driven weighting scheme. With the error distribution unspecified, the proposed estimators share robustness from quantile regression and achieve nearly the same efficiency as the oracle maximum likelihood estimator (MLE) for a variety of error distributions including the normal, mixed-normal, Student’s t , Cauchy distributions, etc. We also suggest an algorithm for the fast implementation of the proposed methodology. Simulations are carried out to compare the performance of different estimators, and the proposed approach is used to analyze the daily S&P 500 Composite index, which verifies the effectiveness and efficiency of our theoretical results. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of sensorless indirect field oriented control of an induction machine. Based on the fourth-order nonlinear model, we propose a novel structure of a full-order Takagi-Sugeno (TS) adaptive observer, through which we ensure the estimation of IM states and the rotor speed, which is considered as an immeasurable premise variable. This approach satisfies global stability for all operating conditions and operates under unknown applied load torque. In this work, a TS fuzzy model is presented to approximate a nonlinear IM model in the stationary rotating reference frame. A TS adaptive observer is then described. We then design observer gains to guarantee specified observer dynamic performances through a D-stability analysis. Meanwhile, a TS-based scheme is proposed for speed estimation. The synthesis is based on sufficient conditions that are developed and formulated into linear matrix inequalities terms. Simulation and experimental results are given to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The paper presents analytic and stochastic methods of structure parameters estimation for a model selection problem. Structure parameters are covariance matrices of parameters of linear and non-linear regression models. To optimize model parameters and structure parameters we maximize a model evidence, a convolution of a data likelihood with a prior distribution of model parameters. The analytic methods are based on the derivatives computation of the approximated model evidence. The stochastic methods are based on the model parameters sampling and data cross-validation. The proposed methods are tested and compared on the synthetic and real data. [ABSTRACT FROM AUTHOR]

In this paper, a new combined model coupling the linear autoregressive moving average (ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed in order to estimate the daily global solar radiation. The main feature of this approach lies in the fact that has the strength to capture the advantages containing in both models. The combined method have been developed and tested using global solar radiation data recorded during two years (2012–2013) for two different climate sites in Algeria. The obtained results showed an improvement of the combined model over ARMA and ANN models in term of mean absolute error (MPE) of about 18.1% and 2.7%, for the first site, of about 27.26% and 1.39% for the second site. Moreover, compared to the ARMA and ANN models, a decrease in the RMSE values of about 17.1% and 3.59% compared to the ARMA and ANN models has been observed. [ABSTRACT FROM AUTHOR]

Compared with the identification of linear structural parameters, it is more difficult to conduct parametric identification of strong nonlinear structural systems, especially when only incomplete structural responses are available. Although the extended Kalman filter (EKF) is useful for structural identification with partial measurements of structural responses and can be extended for the identification of nonlinear structures, EKF approximates nonlinear system through Taylor series expansion and is therefore not effective for the identification of strong nonlinear structural systems. Other approaches such as the unscented Kalman filter (UKF) have been proposed for the identification of strong nonlinear problems. Based on the fact that nonlinearities exist in local areas of structures, a straightforward two-stage identification approach is proposed in this paper for the identification of strong nonlinear structural parameters with incomplete response measurements. In the first stage, the locations of nonlinearities are identified based on the EKF for the identification of the equivalent linear structures. In the second stage, the UKF is utilized to identify the parameters of strong nonlinear structural systems. Therefore, the parametric identification of strong nonlinear structural parameters is simplified by the proposed approach. Several numerical examples with different nonlinear models and locations are used to validate the proposed approach. [ABSTRACT FROM AUTHOR]

In this research, we propose a practical method for simulating the financial return series whose distribution has a specific heaviness. We employ the Ising model for generating financial return series to be analogous to those of the real series. The similarity between real financial return series and simulated one is statistically verified based on their stylized facts including the power law behavior of tail distribution. We also suggest the scheme for setting the parameters in order to simulate the financial return series with specific tail behavior. The simulation method introduced in this paper is expected to be applied to the other financial products whose price return distribution is fat-tailed. [ABSTRACT FROM AUTHOR]

A unidirectional three-mode reduced-order model (ROM) for the lateral motion of a slender and immersed rod subjected to harmonic and axial top motion was derived from the continuum equation of motion. Simple trigonometric functions were employed as approximations for the vibration modes and projection functions in Galerkin׳s method. The non-linear character of the ROM comes from the extensibility of the rod axis and the quadratic hydrodynamic damping. The focus of this investigation is the principal Mathieu׳s instability with respect to the first vibration mode, i.e., the condition in which the top-motion frequency is twice the structural first natural frequency. Time histories of modal amplitude, as well as maps of post-critical steady-state vibration amplitudes were obtained and discussed. It is seen that, within the principal parametric instability region of the first mode, the time history corresponding to the second classic (sinusoidal) mode oscillates with dominant frequency of the first classic (sinusoidal) mode. Another finding is that, besides the principal Mathieu׳s instability region, there are also other regions of instability, but with considerably smaller amplitudes. This aspect is due to the non-linear character of the coupled system of equations that defines the ROM. [ABSTRACT FROM AUTHOR]

A comparison of two nonlinear input-output models describing the relationship between human emotion (excitement, frustration and engagement/boredom) signals and a virtual 3D face feature (distance-between-eyes) is introduced in this paper. A method of least squares with projection to stability domain for the building of stable models with the least output prediction error is proposed. Validation was performed with seven volunteers, and three types of inputs. The results of the modelling showed relatively high prediction accuracy of excitement, frustration and engagement/ boredom signals. [ABSTRACT FROM AUTHOR]

This paper is related to the phenomenon of streaming electrification. The selected results of nonlinear identification of the electrification process are presented. We described the signals used for identification and validation and then two structures of the models used: the nonlinear autoregressive model and the Hammerstein-Wiener model. We then conducted parametric identification and compared the obtained results. We used the Gauss function as the input signal. In the case of the autoregressive model, we used the sigmoid network to estimate the nonlinearity. In the case of the Hammerstein-Wiener model, we tested various types of input nonlinearities. The piecewise linear nonlinearity estimators turned out to be the most effective. Results for the most effective models of both structures: the results of the nonlinear ARX model and the Hammerstein-Wiener model were similar and had varying fit values in the range of 1 to 3 percent. They also had similar generalisation features. In all cases, we also obtained similar shapes of the processes of residues and correlation functions. They indicate a relatively faultless representation of the responses of models as well as certain differences, especially in the end part of the processes representing the given process. [ABSTRACT FROM PUBLISHER]

This paper presents a general theory for the estimations of an entire fatigue curve in ductile materials based on the implicit gradient approach. In order to modify the slope of the Woehler curves, the material was considered non-linear. The average stress of the hysteresis loop was taken into account by means of Walker’s model. Subsequently, the implicit gradient method was adopted for the numerical evaluation of the effective stress and strain at low- and medium-cycle fatigue life and was then related to the fatigue strength of the material. The characteristic length, relating to the fatigue behaviour of the material, was considered constant for the fatigue lifetime. In order to confirm the proposed method, new experimental data were obtained, relating to axisymmetric notched specimens loaded with nominal stress ratio R = −1 and R = 0. In terms of the effective strain amplitude, evaluated by means of the implicit gradient approach, the different Woehler curves of notched specimens were summarised in a unique fatigue curve as a function of Walker’s cycle parameter. [ABSTRACT FROM AUTHOR]

Oftentimes, material components follow non-linear degradation because the material loss trough time is non-linear; the components present different levels of resistance during a degradation process. Constructing predictions of the component life span and performing reliability analyses regarding degradation is possible by non-linear regression. Generally, this method ignores the prediction error in the extrapolated time and it does not account measurement error in the observed sample paths. In this paper, it is proposed new means for predicting time to failure of the components, using a calibration regression method for measuring the error prediction in the extrapolation process. In order to consider the uncertainty, we proposed to perform the estimated times by a trapezoidal fuzzy numbers, providing a more reliable prediction without loss of information. Since the times to failure are shaped by fuzzy numbers, it was adapted the fuzzy probability theory to the classical reliability analysis for estimating the fuzzy reliability of a component. An accelerated test of a polymeric coating process was performed and also an application for a preventive maintenance frequency was proposed. [ABSTRACT FROM AUTHOR]

Progress in optimization algorithms and in computational hardware made deployment of Nonlinear Model Predictive Control (NMPC) and Moving Horizon Estimation (MHE) possible to mechatronic applications. This paper aims to assess the computational performance of NMPC and MHE for rotational start-up of Airborne Wind Energy systems. The capabilities offered by an automatic code generation tool are experimentally verified on a real physical system, using a model comprising 27 states and 4 inputs at a sampling frequency of 25 Hz. The results show the feedback times less than 5 ms for the NMPC with more than 1500 variables. [ABSTRACT FROM AUTHOR]

This paper introduces an original definition of diagnosability for nonlinear dynamical models called functional diagnosability . Fault diagnosability characterizes the faults that can be discriminated using the available sensors in a system. The functional diagnosability definition proposed in this paper is based on analytical redundancy relations obtained from differential algebra tools. Contrary to classical definitions, the study of functional diagnosability highlights some of the analytical redundancy relations properties related to the fault acting on the system. Additionally, it gives a criterion for detecting the faults. Interestingly, the proposed diagnosability definition is closely linked to the notion of identifiability, which establishes an unambiguous mapping between the parameters and the output trajectories of a model. This link allows us to provide a sufficient condition for testing functional diagnosability of a system. Numerical simulations attest the relevance of the suggested approach. [ABSTRACT FROM AUTHOR]

The SOC (state of charge) is the most important index of the battery management systems. However, it cannot be measured directly with sensors and must be estimated with mathematical techniques. An accurate battery model is crucial to exactly estimate the SOC. In order to improve the model accuracy, this paper presents an improved parameter identification method. Firstly, the concept of polarization depth is proposed based on the analysis of polarization characteristics of the lithium-ion batteries. Then, the nonlinear least square technique is applied to determine the model parameters according to data collected from pulsed discharge experiments. The results show that the proposed method can reduce the model error as compared with the conventional approach. Furthermore, a nonlinear observer presented in the previous work is utilized to verify the validity of the proposed parameter identification method in SOC estimation. Finally, experiments with different levels of discharge current are carried out to investigate the influence of polarization depth on SOC estimation. Experimental results show that the proposed method can improve the SOC estimation accuracy as compared with the conventional approach, especially under the conditions of large discharge current. [ABSTRACT FROM AUTHOR]

A series of the simplest thermokinetic models, in which the kinetic subsystem cor- responds to traditional catalytic mechanisms, such as the Eley-Rideal mono- and bimolecular mechanisms and the Langmuir-Hinshelwood mechanism, and autocatalytic mechanisms char- acterized by multiplicity of steady states and autooscillations in the kinetic region, were consid- ered. The method of continuation along a parameter was used for studying these basic models of thermokinetics. The parametric analysis performed allowed us to identify parameter regions characterized by critical effects (multiplicity of steady states and autooscillations). [ABSTRACT FROM AUTHOR]

The nonlinear autoregressive moving average model with exogenous inputs (NARMAX) approach is employed to analyze time-frequency domains for the proton exchange membrane fuel cell (PEMFC) system. The time-domain nonlinear NARMAX model of PEMFC system is modeled from measured experimental input and output data. The outputs obtained by using the time-domain NARMAX model were confirmed by model validation tests which compare the model's predictive outputs with actual system outputs, in an interactive environment. This provides a potentially nondestructive tool for the modeling of a nonlinear PEMFC system. Then, the generalized frequency response function (GFRF) is employed in the frequency-domain, which is computed directly from the time-domain NARMAX model, to provide information about the types of nonlinearities in a PEMFC. The analysis revealed that the time-domain model provides better parameter estimation and a good forecasting ability. The frequency-domain response function is consistent with actual reaction mechanisms. The results illustrate that the time-frequency domain NARMAX approach can be effectively employed in modeling and identifying of PEMFC system. [ABSTRACT FROM AUTHOR]

Internal model control (IMC) design method based on quasi-LPV (Linear Parameter Varying) system is proposed. In this method, the nonlinear model is firstly transformed to the linear model based on quasi-LPV method; then, the quadrotor nonlinear motion function is transformed to transfer function matrix based on the transformation model from the state space to the transfer function; further, IMC is designed to control the controlled object represented by transfer function matrix and realize quadrotor trajectory tracking. The performance of the controller proposed in this paper is tested by tracking for three reference trajectories with drastic changes. The simulation results indicate that the control method proposed in this paper has stronger robustness to parameters uncertainty and disturbance rejection performance. [ABSTRACT FROM AUTHOR]

We propose a nonlinear elasticity model for vesicle membranes which is an Eulerian version of a model introduced by Pantz and Trabelsi. We describe the limit behavior of sequences of configurations whose energy goes to 0 in a fixed domain. The material is highly anisotropic and the analysis is based on some rigidity estimates adapted to this anisotropy. The main part of the paper is devoted to these estimates and to some of their consequences. The strongest form of these estimates are used in a second article to derive the thin-shell limit bending theory of the model. [ABSTRACT FROM AUTHOR]

Modeling of space manipulator joints has been studied for years but accurate positioning control is still unsatisfactory. One of the primary reasons is that, in the past researches, effects of the high-ratio reducers in the joints have usually been neglected. In this paper, a nonlinear dynamic model of the manipulator joint with planetary gear train transmission is developed by considering time-variant joint stiffness, backlash and reduction ratio. Based on the gear parameters and meshing phase relationship, the stiffness of the joint model is presented, in which the time-variant stiffness of 2K-H planetary gear train and the backlash are taken into consideration. The backlash effect is modeled as an alternate engagement mechanism, and the transmitted torque is defined as a dead zone function. This model is simulated on a two-link space manipulator system. The results show that the time-variant stiffness effect can be simplified as a constant value in most cases when other shafting parts are flexible, while if the total stiffness is approximate to the nonlinear stiffness, the positioning accuracy is reduced if neglecting the time-variant part. On the other hand, the backlash is the main source of positioning error and impact. Minimizing backlash is the most effective way to improve positioning accuracy and avoid the impact in the gearing system. [ABSTRACT FROM AUTHOR]