Dynamic Friction Parameter Identification Method with LuGre Model for Direct-Drive Rotary Torque Motor

Wang, Xingjian; Lin, Song; Wang, Shaoping

doi:10.1155/2016/6929457

Cited by 18 publications

(16 citation statements)

References 25 publications

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“…Parameter calibration is commonly achieved by solving an optimization problem to find deterministic values for each model parameter to be calibrated that best fit the chosen calibration criteria. For example in [8], parameters of a friction model were identified for a direct-drive rotary torque motor using the Novel Evolutionary Algorithm optimization. Experimental data from a test rig was used within the optimization process and two objective functions were minimized for different parameter sets.…”

Section: Introductionmentioning

confidence: 99%

Forward vs. Bayesian Inference Parameter Calibration: Two Approaches for Non-deterministic Parameter Calibration of a Beam-Column Model

Schaeffner

Gehb

Feldmann

et al. 2021

Lecture Notes in Mechanical Engineering

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Mathematical models are commonly used to predict the dynamic behavior of mechanical structures or to synthesize controllers for active systems. Calibrating the model parameters to experimental data is crucial to achieve reliable and adequate model predictions. However, the experimental dynamic behavior is uncertain due to variations in component properties, assembly and mounting. Therefore, uncertainty in the model parameters can be considered in a non-deterministic calibration. In this paper, we compare two approaches for a non-deterministic parameter calibration, which both consider uncertainty in the parameters of a beam-column model. The goal is to improve the model prediction of the axial load-dependent lateral dynamic behavior. The investigation is based on a beam-column system subjected to compressive axial loads used for active buckling control. A representative sample of 30 nominally identical beam-column systems characterizes the variations in the experimental lateral axial load-dependent dynamic behavior. First, in a forward parameter calibration approach, the parameters of the beam-column model are calibrated separately for all 30 investigated beam-column systems using a least squares optimization. The uncertainty in the parameters is obtained by assuming normal distributions of the separately calibrated parameters. Second, in a Bayesian inference parameter calibration approach, the parameters are calibrated using the complete sample of experimental data. Posterior distributions of the parameters characterize the uncertain dynamic behavior of the beam-column model. For both non-deterministic parameter calibration approaches, the predicted uncertainty ranges of the axial load-dependent lateral dynamic behavior are compared to the uncertain experimental behavior and the most accurate results are identified.

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Section: Introductionmentioning

confidence: 99%

Forward vs. Bayesian Inference Parameter Calibration: Two Approaches for Non-deterministic Parameter Calibration of a Beam-Column Model

Schaeffner

Gehb

Feldmann

et al. 2021

Lecture Notes in Mechanical Engineering

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show abstract

“…Thus, model parameter calibration adjusts the model parameters to physical observations of experimentally observed data. A deterministic model parameter calibration solves an optimization problem, e.g., [5], to achieve a best fit for the unknown model parameters based on a defined calibration criterion. Consequently, there is no information about the model parameter uncertainty after the calibration procedure.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Model Uncertainty in the Prediction of the Vibroacoustic Behavior of a Rectangular Plate by Means of Bayesian Inference

Kleinfeller

Gehb

Schaeffner

et al. 2021

Lecture Notes in Mechanical Engineering

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Designing the vibroacoustic properties of thin-walled structures is of particularly high practical relevance in the design of vehicle structures. The vibroacoustic properties of thin-walled structures, e.g., vehicle bodies, are usually designed using finite element models. Additional development effort, e.g., experimental tests, arises if the quality of the model predictions are limited due to inherent model uncertainty. Model uncertainty of finite element models usually occurs in the modeling process due to simplifications of the geometry or boundary conditions. The latter highly affect the vibroacoustic properties of a thin-walled structure. The stiffness of the boundary condition is often assumed to be infinite or zero in the finite element model, which can lead to a discrepancy between the measured and the calculated vibroacoustic behavior. This paper compares two different boundary condition assumptions for the finite element (FE) model of a simply supported rectangular plate in their capability to predict the vibroacoustic behavior. The two different boundary conditions are of increasing complexity in assuming the stiffness. In a first step, a probabilistic model parameter calibration via Bayesian inference for the boundary conditions related parameters for the two FE models is performed. For this purpose, a test stand for simply supported rectangular plates is set up and the experimental data is obtained by measuring the vibrations of the test specimen by means of scanning laser Doppler vibrometry. In a second step, the model uncertainty of the two finite element models is identified. For this purpose, the prediction error of the vibroacoustic behavior is calculated. The prediction error describes the discrepancy between the experimental and the numerical data. Based on the distribution of the prediction error, which is determined from the results of the probabilistic model calibration, the model uncertainty is assessed and the model, which most adequately predicts the vibroacoustic behavior, is identified.

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“…To reduce the negative effect of friction interference on robot’s control performance, some scholars have proposed compensation methods for friction interference. 16 –18 For example, Luo et al 16 designed a disturbance observer based on RBFNN to compensate for friction interference; however, the linear disturbance observer is only effective for a certain bandwidth signal but not enough for the friction signal acting on the whole bandwidth region. Ufnalski and Grzesiak 17 designed a feed forward compensation method for friction interference based on neural network, but the speed tracking signal brought compensation error.…”

Section: Introductionmentioning

confidence: 99%

Adaptive block compensation trajectory tracking control based on LuGre friction model

2019

International Journal of Advanced Robotic Systems

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Aiming at the problems of modeling error and uncertain external disturbance in the multi-joint robot control model, an adaptive block compensation trajectory tracking controller based on LuGre friction model is proposed. Firstly, the algorithm divides the interference term of LuGre friction model into three parts with different physical quantities. Secondly, an adaptive neural network compensator is designed to assess the three parts of the LuGre friction model. Thirdly, a robust sliding mode controller is developed to reduce the influence of these estimation errors of neural network compensator and other uncertain disturbances and ensure that the system converges in a finite time at the same time. Finally, numerical simulations under different input and disturbance signals for the planar multi-joint robot and the inverted pendulum are conducted to validate the effectiveness of the proposed controller, and the performance of the proposed controller is compared with conventional sliding mode controller to illustrate the usefulness and efficiency of the proposed controller.

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Dynamic Friction Parameter Identification Method with LuGre Model for Direct-Drive Rotary Torque Motor

Cited by 18 publications

References 25 publications

Forward vs. Bayesian Inference Parameter Calibration: Two Approaches for Non-deterministic Parameter Calibration of a Beam-Column Model

Forward vs. Bayesian Inference Parameter Calibration: Two Approaches for Non-deterministic Parameter Calibration of a Beam-Column Model

Assessment of Model Uncertainty in the Prediction of the Vibroacoustic Behavior of a Rectangular Plate by Means of Bayesian Inference

Adaptive block compensation trajectory tracking control based on LuGre friction model

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