A review of friction damping modeling and testing

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From model-driven to data-driven : a review of hysteresis modeling in structural and mechanical systems

Hysteresis is a natural phenomenon that widely exists in structural and mechanical systems. The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous methods have been developed to describe hysteresis. In this paper, a review of the available hysteretic modeling methods is carried out. Such methods are divided into: a) model-driven and b) datadriven methods. The model-driven method uses parameter identification to determine parameters. Three types of parametric models are introduced including polynomial models, differential based models, and operator based models. Four algorithms as least mean square error algorithm, Kalman filter algorithm, metaheuristic algorithms, and Bayesian estimation are presented to realize parameter identification. The data-driven method utilizes universal mathematical models to describe hysteretic behavior. Regression model, artificial neural network, least square support vector machine, and deep learning are introduced in turn as the classical data-driven methods. Model-data driven hybrid methods are also discussed to make up for the shortcomings of the two methods. Based on a multi-dimensional evaluation, the existing problems and open challenges of different hysteresis modeling methods are discussed. Some possible research directions about hysteresis description are given in the final section

Artificial intelligent based friction modelling and compensation in motion control system

The interest in the study of friction in control engineering has been driven by the need for 10 precise motion control in most of industrial applications such as machine tools, robot 11 systems, semiconductor manufacturing systems and Mechatronics systems. Friction has 12 been experimentally shown to be a major factor in performance degradation in various 13 control tasks. Among the prominent effects of friction in motion control are: steady state 14 error to a reference command, slow response, periodic process of sticking and sliding (stick-15 slip) motion, as well as periodic oscillations about a reference point known as hunting when 16 an integral control is employed in the control scheme. Table 1 shows the effects and type of 17 friction as highlighted by Armstrong et. al.(1994). It is observed that, each of task is 18 dominated by at least one friction effect ranging from stiction, or/and kinetic to negative 19 friction (Stribeck). Hence, the need for accurate compensation of friction has become 20 important in high precision motion control. Several techniques to alleviate the effects of 21 friction have been reported in the literature (Dupont and Armstrong, 1993; Wahyudi, 2003; 22 Tjahjowidodo, 2004; Canudas, et. al., 1986). 23 One of the successful methods is the well-known model-based friction compensation 24 (Armstrong et al., 1994; Canudas de Wit et al., 1995 and Wen-Fang, 2007). In this method, 25 the effect of the friction is cancelled by applying additional control signal which generates a 26 torque/force. The generated torque/force has the same value (or approximately the same) 27 with the friction torque/force but in opposite direction

Friction identification and compensation on nanometer scale

This work concerns the modelling and experimental verification of the highly nonlinear friction behavior in positioning on the nanometer scale. The main goal of this work is to adjust and identify a simple dynamic friction model which allows a model-based estimation of the friction force in combination with the system inertia against displacement. Experiments in the pre-sliding and sliding friction regimes are conducted on an experimental setup. A hybrid two-stage parameter estimation algorithm is used to fit the model parameters based on the experimental data. Finally, the identified friction model is utilized as a model-based feedforward controller combined with a classical feedback controller to compensate the nonlinear friction force and reduce tracking errors

Friction identification and compensation on nanometer scale

This work concerns the modelling and experimental verification of the highly nonlinear friction behavior in positioning on the nanometer scale. The main goal of this work is to adjust and identify a simple dynamic friction model which allows a model-based estimation of the friction force in combination with the system inertia against displacement. Experiments in the pre-sliding and sliding friction regimes are conducted on an experimental setup. A hybrid two-stage parameter estimation algorithm is used to fit the model parameters based on the experimental data. Finally, the identified friction model is utilized as a model-based feedforward controller combined with a classical feedback controller to compensate the nonlinear friction force and reduce tracking errors

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

Attainment of high-performance motion/velocity control objectives for the Direct-Drive Rotary (DDR) torque motor should fully consider practical nonlinearities in controller design, such as dynamic friction. The LuGre model has been widely utilized to describe nonlinear friction behavior; however, parameter identification for the LuGre model remains a challenge. A new dynamic friction parameter identification method for LuGre model is proposed in this study. Static parameters are identified through a series of constant velocity experiments, while dynamic parameters are obtained through a presliding process. Novel evolutionary algorithm (NEA) is utilized to increase identification accuracy. Experimental results gathered from the identification experiments conducted in the study for a practical DDR torque motor control system validate the effectiveness of the proposed method

Experimental identification of friction model parameters for selected materials

The work has been supported by the project “Identification and compensation of imperfections and friction effects in joints of mechatronic systems” No. 23-07280S of the Czech Science Foundation

Position control on nanometer scale based on an adaptive friction compensation scheme

This work concerns a non-model-based friction compensation scheme for dynamic position control on nanometer scale. The main goal of this work is to build up and implement a simple dynamic friction observer which allows an estimation of the friction force in combination with the system inertia against displacement. Experiments in the pre-sliding and sliding friction regimes are con-ducted on an experimental setup. After a short review of friction compensation, the experimental setup is explained in detail. Next, the observer is modeled mathematically and the used control scheme is presented. Finally, the friction observer is utilized as a non-model-based friction estimator combined with a classical feedback controller to compensate the nonlinear friction force and reduce tracking errors significantly. It is shown that the proposed controlling approach is able to realize a fast and ultra precise positioning over long distances