Influence Of Model Error Research Articles

Identification of dynamic parameters of journal bearings in an asymmetric rotor-bearing system

PurposeThe purpose of this study is to identify the dynamic parameters of journal bearings in asymmetric rotor systems without additional test runs or excitations.Design/methodology/approachAn asymmetric rotor-bearing test rig was set up for the identification experiment. Comparations were made between the measured response of the asymmetric rotor and the symmetric rotor. The mathematical model of the asymmetric rotor is established by the finite element method. The identification algorithm is based on the model of the rotor and the measured vibration response to identify bearing parameters. The influence of modeling error and measurement noise on the identification results are numerically analyzed. The dynamic parameters of the journal bearings under different rotational speeds are identified and compared with the theoretical values calculated by the perturbation method.FindingsThe experiment results show that the vibration characteristics of the asymmetric rotor and the symmetric rotor are different. The numerical evaluation of the identification algorithm shows that the algorithm is accurate and has good robustness to modeling error and measurement noise. The identified dynamic parameters agree reasonably well with the parameters derived from the theoretical bearing model.Originality/valueThe proposed identification method uses the unique vibration characteristics of asymmetric rotors to identify the bearing dynamic parameters. As the method does not require excitations or additional test runs, it is suitable for the field test.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0096/

Robust State Estimation for Uncertain Discrete Linear Systems with Delayed Measurements

Measurement delays and model parametric uncertainties are meaningful issues in actual systems. Addressing the simultaneous existence of random model parametric uncertainties and constant measurement delay in the discrete-time linear systems, this study proposes a novel robust estimation method based on the combination of Kalman filter regularized least-squares (RLS) framework and state augmentation. The state augmentation method is elaborately designed, and the cost function is improved by considering the influence of modelling errors. A recursive program similar to the Kalman filter is derived. Meanwhile, the asymptotic stability conditions of the proposed estimator and the boundedness conditions of its error covariance are analyzed theoretically. Numerical simulation results show that the proposed method has a better processing capability for measurement delay and better robustness to model parametric uncertainties than the Kalman filter based on nominal parameters.

Practical consequences of inertia shaping for interaction and tracking in robot control

In trajectory tracking and interaction control of robots, two fundamentally different concepts define the boundaries within which most nonlinear model-based approaches can be located. On the one hand controllers such as the PD+ preserve the natural inertia and avoid feedback of external forces and torques. On the other hand controllers based on feedback linearization, as used in most inverse dynamics approaches, enforce linear closed-loop dynamics by means of external force/torque feedback. Here, these two basic concepts of keeping and shaping of the natural inertia are investigated and compared including aspects such as interaction behavior, tracking performance, tuning parameters, influence of modeling errors, and effective feedback gains. Exemplary case studies on a standard torque-controlled robot are performed. The understanding of these features and differences is of major importance for the proper selection and deployment of interaction and tracking controllers in practice.

Design of Sliding Mode Controller for Tilting Quadrotor UAV Based on Predetermined Performance

Aiming at the influence of modeling error and uncertain interference such as external sidewind in the control system of tilting quadrotor UAV, a non-singular fast terminal sliding mode controller based on predetermined performance is designed for its vertical take-off and landing mode. Firstly, based on the classical mechanics theory, the nonlinear dynamics model of tilt-rotor UAV in vertical take-off and landing mode was established. Then, on the basis of the non-singular fast terminal sliding mode controller, the predetermined performance function was introduced to make the system error within the specified performance range, and the final bounded convergence of the tracking error in the closed-loop system was proved based on the Lyapunov stability theory. Finally, the simulation experiment was carried out in MATLAB/Simulink. The simulation results show that the designed method can still achieve the stable tracking of the predetermined trajectory under the circumstance of uncertain external interference, and it has good control performance and anti-interference ability.

Novel Soil Moisture Estimates Combining the Ensemble Kalman Filter Data Assimilation and the Method of Breeding Growing Modes

Soil moisture plays an important role in climate prediction and drought monitoring. Data assimilation, as a method of integrating multi-geographic spatial data, plays an increasingly important role in estimating soil moisture. Model prediction error, an important part of the background field information, occupies a position that could not be ignored in data assimilation. The model prediction error in data assimilation consists of three parts: forcing data error, initial field error, and model error. However, the influence of model error in current data assimilation methods has not been completely considered in many studies. Therefore, we proposed a theoretical framework of the ensemble Kalman filter (EnKF) data assimilation based on the breeding of growing modes (BGM) method. This framework used the BGM method to perturb the initial field error term w of EnKF, and the EnKF data assimilation to assimilate the data to obtain the soil moisture analysis value. The feasibility and superiority of the proposed framework were verified, taking into consideration breeding length and ensemble size through experiments. We conducted experiments and evaluated the accuracy of the BGM and the Monte Carlo (MC) methods. The experiment showed that the BGM method could improve the estimation accuracy of the assimilated soil moisture and solve the problem of model error which is not fully expressed in data assimilation. This study can be widely used in data assimilation and has a significant role in weather forecast and drought monitoring.

Performance enhancement of multivariable model reference optimal adaptive motor speed controller using error-dependent hyperbolic gain functions

The main contribution of this paper is to formulate a robust-adaptive and stable state-space speed control strategy for DC motors. The linear-quadratic-integral (LQI) controller is utilized as the baseline controller for optimal speed-regulation, accurate reference-tracking and elimination of steady-state fluctuations in the motor’s response. To reject the influence of modelling errors, the LQI controller is augmented with a Lyapunov-based model reference adaptation system (MRAS) that adaptively modulates the controller gains while maintaining the asymptotic stability of the controller. To further enhance the system’s robustness against parametric uncertainties, the adaptation gains of MRAS online gain-adjustment law are dynamically adjusted, after every sampling interval, using smooth hyperbolic functions of motor’s speed-error. This modification significantly improves the system’s response-speed and damping against oscillations, while ensuring its stability under all operating conditions. It dynamically re-configures the control-input trajectory to enhance the system’s immunity against the detrimental effects of random faults occurring in practical motorized systems such as bounded impulsive-disturbances, modelling errors, and abrupt load–torque variations. The efficacy of the proposed control strategy is validated by conducting credible hardware-in-the-loop experiments on QNET 2.0 DC Motor Board. The experimental results successfully validate the superior tracking accuracy and disturbance-rejection capability of the proposed control strategy as compared to other controller variants benchmarked in this article.

Model-based quality assurance in railway infrastructure planning

Abstract A primary motive for adopting the methodology Building Information Modeling (BIM) in planning processes is to improve planning accuracy, cost security, and in turn quality. Up to now, however, a generally applicable, standardized means of validating design quality has been lacking. To address this shortcoming, this article presents 14 quality parameters in the domains of clash detection, semantics as well as quantities and costs, that apply to the field of infrastructure planning. The sets of rules outlined in the article are adaptable and extendable in order to respond flexibly to different model structures. The investigation focuses on how important and recurring tests can be carried out automatically and how to make the results analyzable in a transparent and standardized manner. The proposed concept thoroughly extends well-known methods such as attribute testing and clash detection analysis of the 3D model. Doing so, the paper presents a set of novel methods for quality assurance, including 4D clash detection and checks for semantic-geometric coherence. The paper discusses in detail: the influence of modeling errors on clash detection, the difference of 3D and 4D clashes, formal methods for checking the correct linkage between 3D BIM and the bill of quantities, formal approaches for checking the semantic-geometric coherence of BIM objects. The quality assurance concept presented in the article concludes with a standardized evaluation for the individual quality criteria using a school grading system, traffic light, and percentage scale. Finally, the concept is applied in a comprehensive case study on a large-scale infrastructure project and the results of the formal quality assessment are presented.

Research on active steering control strategy based on sideslip angle estimation for a steering‐by‐wire forklift

Taking the TFC35 electric forklift as the research object, we analyze the movements and forces of the forklift in three directions: yaw, lateral movement, and roll. Combined with the load transfer formula of the forklift and the nonlinear and linear tire model, the formula for lateral force of the four tires of forklift is deduced, and the nonlinear and linear three‐degree‐of‐freedom (3DOF) forklift dynamics model is established. Because the sideslip angle of the forklift is not easy to measure directly, based on the linear 3DOF forklift model, we take the yaw rate and lateral angle as the observation variables and consider the influence of model error. An adaptive Kalman filter is designed to estimate the sideslip angle of the forklift. In order to improve the accuracy of estimation, an online optimization strategy of the adaptive Kalman filter based on genetic algorithm with an adaptive factor is proposed. Simulation results show the effectiveness of the proposed method. In combination with the forklift reference model, the rear wheel angle of the forklift is corrected by comprehensive proportional feedback of yaw rate and the sideslip angle, and the active steering system for a steering‐by‐wire forklift is designed. Simulation and test results show that the control strategy of forklift's active steering system can improve its driving stability and handling performance. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Linearized reconstruction for diffuse optical spectroscopic imaging

In this paper, we present a novel reconstruction method for diffuse optical spectroscopic imaging with a commonly used tissue model of optical absorption and scattering. It is based on linearization and group sparsity, which allows the diffusion coefficient and absorption coefficient to be recovered simultaneously, provided that their spectral profiles are incoherent and a sufficient number of wavelengths are judiciously taken for the measurements. We also discuss the reconstruction for an imperfectly known boundary and show that, with multi-wavelength data, the method can reduce the influence of modelling errors and still recover the absorption coefficient. Extensive numerical experiments are presented to support our analysis.

Probabilistic updating of fishbone model for assessing seismic damage to beam–column connections in steel moment‐resisting frames

AbstractThis article presents a probabilistic method of updating fishbone models for assessing seismic damage on beam–column connections in steel moment‐resisting frames. Fishbone models enable explicitly identifying the rotational stiffness of beams, which is not possible with a shear building model commonly used in Bayesian model updating approaches. Necessary formulations to utilize fishbone models for model updating with measured floor accelerations under small‐amplitude loadings including ambient excitations were first formulated. To accommodate the incompleteness of modal data to update unknown parameters of fishbone models, that is, the stiffness of rotational springs, a hierarchical Bayesian model updating algorithm is implemented. Seismic damage of beam–column connections are estimated by making a comparison of the identified rotational stiffness of springs in the fishbone models before and after earthquakes. The effectiveness of the proposed method is first examined with numerical simulations using a 10‐story building model. Then, the applicability to realistic beam damage, that is, not artificially introduced, is evaluated through a full‐scale steel frame test at the E‐Defense shaking table facility. The article also discusses coefficients of variation of the identified stiffness and influence of modeling error on estimation of realistic damage to check the credibility of the method for real‐life applications.

Effect of model errors on the closed orbit correction at the SIS18 Synchrotron of GSI

The influence of model errors on the closed orbit correction for the SIS18 synchrotron at GSI has been simulated. The systematic model drift over the ramp due to the transition of triplet to doublet quadrupole configuration and the non-systematic tune shifts due to image charge and beta beating are considered. The study is aimed to draw hints for the robust stability requirements of the closed orbit feedback controller against model mismatch.

Noise and Parameter Heterogeneity in Aggregate Models of Thermostatically Controlled Loads

Aggregate models are used in the analysis and control of large populations of thermostatically controlled loads (TCLs), such as air-conditioners and water heaters. The fidelity of such models is studied by analyzing the influences of noise and parameter heterogeneity on TCL aggregate dynamics. While TCLs can provide valuable services to the power systems, control may cause their temperatures to synchronize, which may then lead to undesirable power oscillations. Recent works has shown that the aggregate dynamics of TCLs can be modeled by tracking the evolution of probability densities over discrete temperature ranges or bins. To accurately capture oscillations in aggregate power, such bin-based models require a large number of bins. The process of obtaining the Markov state transition matrix that governs the dynamics can be computationally intensive when using Monte Carlo based system identification techniques. Existing analytical techniques are further limited as noise and heterogeneity in several thermal parameters are difficult to incorporate. These challenges are addressed by developing a fast analytical technique that incorporates noise and heterogeneity into bin-based aggregate models. Results show the identified and the analytical models match very closely. Studies consider the influence of model error, noise and parameter heterogeneity on the damping of oscillations.

Soot aggregate sizing through multiangle elastic light scattering: Influence of model error

Inferring the size distribution and morphology of aerosolized soot aggregates from the angular distribution of elastically-scattered light involves solving an ill-posed inverse problem. Light scattering is often approximated using Rayleigh-Debye-Gans Fractal Aggregate (RDG-FA) theory, which is computationally-efficient but limited in accuracy. The resulting model errors are amplified by the ill-posed nature of the problem into large errors in the recovered soot parameters. More precise approaches, like the multi-sphere T-Matrix (MSTM) method, are too computationally-intensive to use in the inference procedure. The efficiency of RDG-FA and the accuracy of MSTM can be combined by modeling the approximation error. The error function is derived from a principal component analysis on error matrices generated for randomly-sampled aggregates having morphological fractal parameters sampled from distributions derived from published studies in the literature. The error model is then used to correct the RDG-FA kernel in the forward model for a particular set of fractal parameters. Finally, the corrected model is used to estimate probability densities of the size distribution and aggregate fractal parameters via Bayesian inference.

Influence of model errors in optimal sensor placement

The paper investigates the role of model errors and parametric uncertainties in optimal or near optimal sensor placements for structural health monitoring (SHM) and modal testing. The near optimal set of measurement locations is obtained by the Information Entropy theory; the results of placement process considerably depend on the so-called covariance matrix of prediction error as well as on the definition of the correlation function. A constant and an exponential correlation function depending on the distance between sensors are firstly assumed; then a proposal depending on both distance and modal vectors is presented. With reference to a simple case-study, the effect of model uncertainties on results is described and the reliability and the robustness of the proposed correlation function in the case of model errors are tested with reference to 2D and 3D benchmark case studies. A measure of the quality of the obtained sensor configuration is considered through the use of independent assessment criteria. In conclusion, the results obtained by applying the proposed procedure on a real 5-spans steel footbridge are described. The proposed method also allows to better estimate higher modes when the number of sensors is greater than the number of modes of interest. In addition, the results show a smaller variation in the sensor position when uncertainties occur.

Study on the onset and development of the three-dimensional separation around a diamond wing with incompressible flow solvers

One of the challenges in simulating aerodynamic flows is the accurate prediction of onset and progression of separation. In earlier STO research, it was found that various predictions for three-dimensional airfoils at angle of attack showed different pitch moments due to differences in the predicted location of separation areas. Therefore, a diamond shaped wing was developed within the NATO STO AVT-183 research group, to isolate the blunt leading edge separation and understand the underlying physical mechanisms. In the present article, two viscous-flow solvers dedicated to hydrodynamic applications are used to predict the flow around the diamond wing. Both codes solve the incompressible flow using unstructured grids and finite volume discretization on the same grids. The results comprise a study of with/without peniche configurations and different turbulence models ranging from isotropic or anisotropic statistical turbulence closures to hybrid LES turbulence models. Moreover, the role played by the discretization error is assessed by using anisotropic automatic grid refinement procedures based on flow-related criteria. A detailed discussion is made, highlighting the similarities and differences of the results, the influence of modeling errors and showing the potential of CFD tools to predict the type of flow under consideration.

A Fuzzy PID Control Method for the Grasping Force of an Underactuated Prosthetic Hand

The prosthetic hand which is the auxiliary equipment of the disabled people in the daily life must ensure stable grasping. Therefore, grasping force control with high accuracy and fast response is the most significant demand. With a conventional PID control, nonlinear dynamic and modeling error of prosthetic hand highly affect the control performance, causing deviation of desired force and slow response. A fuzzy PID control strategy is proposed for eliminating the influence of modeling error and implementing the grasping force control. The PID controller parameters are tuned automatically, on-line, to improve the control performance of the prosthetic hand system. The simulation and experimental results are presented to demonstrate the reliability and effectiveness of the proposed control scheme.

Safety Analysis of the Patch Load Resistance of Plate Girders: Influence of Model Error and Variability

This study aims to undertake a statistical study to evaluate the accuracy of nine models that have been previously proposed for estimating the ultimate resistance of plate girders subjected to patch loading. For each model, mean errors and standard errors, as well as the probability of underestimating or overestimating patch load resistance, are estimated and the resultant values are compared one to another. Prior to that, the models are initially calibrated in order to improve interaction formulae using an experimental data set collected from the literature. The models are then analyzed by computing design factors associated with a target risk level (probability of exceedance). These models are compared one to another considering uncertainties existed in material and geometrical properties. The Monte Carlo simulation method is used to generate random variables. The statistical parameters of the calibrated models are calculated for various coefficients of variations regardless of their correlation with the random resistance variables. These probabilistic results are very useful for evaluating the stochastic sensitivity of the calibrated models.

Large-eddy Simulation of Triangular-stabilized Lean Premixed Turbulent Flames: Quality and Error Assessment

In this numerical study, an algebraic flame surface wrinkling (AFSW) reaction submodel based on the progress variable approach is implemented in the large-eddy simulation (LES) context and validated against the triangular stabilized bluff body flame configuration measurements i.e. in VOLVO test rig. The quantitative predictability of the AFSW model is analyzed in comparison with another well validated turbulent flame speed closure (TFC) combustion model in order to help assess the behaviour of the present model and to further help improve the understanding of the flow and flame dynamics. Characterization of non-reacting (or cold) and reacting flows are performed using various subgrid scale models for consistent grid size variation with 300,000 (coarse), 1.2 million (intermediate) and 2.4 million (fine) grid cells. For non-reacting flows at inlet velocity of 17 m/s and inlet temperature 288 K, coarse grid leads to over prediction of turbulence quantities due to low dissipation at the early stage of flow development behind the bluff body that convects downstream eventually polluting the resulting solution. The simulated results with the intermediate (and fine) grid for mean flow and turbulence quantities, and the vortex shedding frequency (fs) closely match experimental data. For combusting flows for lean propane/air mixtures at 35 m/s and 600 K, the vortex shedding frequency increase threefold compared with cold scenario. The predicted results of mean, rms velocities and reaction progress variable are generally in good agreement with experimental data. For the coarse grid the combustion predictions show a shorter recirculation region due to higher turbulent burning rate. Finally, both cold and reacting LES data are analyzed for uncertainty in the solution using two quality assessment techniques: two-grid estimator by Celik, and model and grid variation by Klein. For both approaches, the resolved turbulent kinetic energy is used to estimate the grid quality and error assessment. The quality assessment reveals that the cold flows are well resolved even on the intermediate mesh, while for the reacting flows even the fine mesh is locally not sufficient in the flamelet region. The Klein approach estimates that depending on the recirculation region in cold scenario both numerical and model errors rise near the bluff-body region, while in combusting flows these errors are significant behind the stabilizing point due to preheating of unburned mixture and reaction heat release. The total error mainly depends on the numerical error and the influence of model error is low for this configuration.

H<sub>∞</sub> Control of Flexible Joint Robot via T-S Fuzzy Model

In this paper, a H∞ control approach based on T-S fuzzy model for flexible joint robot is proposed. First, the Takagi and Sugeno (T-S) fuzzy model is applied to approximate the flexible joint robot. Then, a fuzzy controller is developed based on parallel distributed compensation principle (PDC), and H∞ performance is employed to restrain the influence of modeling error caused by variety of stiffness. The stability conditions for the flexible joint robot control system are proposed by Lyapunov function. Finally, the simulation results are given to show the effectiveness of the proposed method.

Modelling errors, entropy and the hydraulic reliability of water distribution systems

This paper reports on an investigation of the possible influence of modelling errors on the relationship between the entropy and hydraulic reliability of water distribution systems. The errors are due to minor differences between the design optimisation and subsequent simulation models, which lead to small discrepancies between the capacity of the network and the required supply. Pressure-dependent analysis was used for the hydraulic simulations. It is shown that any correlation between the redundancy or undercapacity due to the modelling errors and the hydraulic reliability is insignificant. The results, therefore, provide yet more evidence that the entropy-reliability relationship is strong.