Precise friction control for the nonlinear friction system using the friction state observer and sliding mode control with recurrent fuzzy neural networks

Kim, Han Me; Park, Soo Hong; Han, Seong-Ik

doi:10.1016/j.mechatronics.2009.04.004

Cited by 38 publications

(22 citation statements)

References 19 publications

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“…e goodness-of-fit and errors between actual friction and model prediction for three kinds of models from equation 2to equation (4) are presented in Table 1.…”

Section: E Effects Of Velocity Load and Temperature On Staticmentioning

confidence: 99%

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An Optimized RBF Neural Network Based on Beetle Antennae Search Algorithm for Modeling the Static Friction in a Robotic Manipulator Joint

Wang

Chen

et al. 2020

Mathematical Problems in Engineering

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Friction is a nonlinear effect that occurs in all mechanical systems which may cause limit cycles, tracking errors, and other undesirable effects. Traditional static friction models cannot characterize all the friction situations. In recent years, neural network (NN) technique has been widely used to approximate the nonlinear function. In this paper, a new method which combines radical basis function neural network (BRFNN) with beetle antennae search (BAS) algorithm for modeling friction in a robotic joint is proposed. Velocity, load, and temperature are considered as the three factors that influence the static friction. It is shown that the proposed BAS-RBFNN possesses better performance in terms of faster convergence rate and higher accuracy.

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“…e goodness-of-fit and errors between actual friction and model prediction for three kinds of models from equation 2to equation (4) are presented in Table 1.…”

Section: E Effects Of Velocity Load and Temperature On Staticmentioning

confidence: 99%

“…erefore, a precise friction model may considerably improve the efficiency for control purposes and other applications [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An Optimized RBF Neural Network Based on Beetle Antennae Search Algorithm for Modeling the Static Friction in a Robotic Manipulator Joint

Wang

Chen

et al. 2020

Mathematical Problems in Engineering

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show abstract

“…One reason for the interest in friction of manipulator joints is the need to model friction for control purposes [4][5][6][7][8], where a precise friction model can considerably improve the overall performance of a manipulator with respect to accuracy and control stability. Since friction can relate to the wear down process of mechanical systems [9], including robot joints [10], there is also interest in friction modeling for robot condition monitoring and fault detection [10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Static Friction in a Robot Joint—Modeling and Identification of Load and Temperature Effects

Bittencourt¹,

Gunnarsson

2012

Journal of Dynamic Systems, Measurement, and Control

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Friction is the result of complex interactions between contacting surfaces in down to a nanoscale perspective. Depending on the application, the different models available are more or less suitable. Static friction models are typically considered to be dependent only on relative speed of interacting surfaces. However, it is known that friction can be affected by other factors than speed. In this paper, the typical friction phenomena and models used in robotics are reviewed. It is shown how such models can be represented as a sum of functions of relevant states which are linear and nonlinear in the parameters, and how the identification method described in Ref. [1] can be used to identify them when all states are measured. The discussion follows with a detailed experimental study of friction in a robot joint under changes of joint angle, load torque, and temperature. Justified by their significance, load torque and temperature are included in an extended static friction model. The proposed model is validated in a wide operating range, considerably improving the prediction performance compared to a standard model.

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“…Such applications have been widely reported in motor control [2], servo systems [3], robotic systems [4] and other fields [5,6]. Many modem control methodologies such as nonlinear control [7], optimal control [8], adaptive control [9], and variable structure control [10] have been tried to improve the system performances. However, there is still much effort left to be studied.…”

Section: Introductionmentioning

confidence: 99%

Tracker design using composite nonlinear feedback with application to a DC servomotor

Mobayen

Majd

2012

2012 3rd Power Electronics and Drive Systems Technology (PEDSTC)

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In this paper, the theory of a nonlinear control technique, i.e., the composite nonlinear feedback control is considered for robust tracking of a class of linear systems with time varying uncertain parameters and disturbances. This controller can guarantee that the tracking error decreases asymptotically to zero in the presence of time varying uncertain parameters and disturbances. For performance improvement of dynamical system, this proposed robust tracking controller consists of linear and nonlinear feedback parts without any switching element. The linear feedback law is designed to yield a closed loop system with a small damping ratio for the existence of a quick response. On the other hand, the nonlinear feedback law is designed to increase the damping ratio as the system output approaches the output of the reference model. Finally, the simulations of applying the control law on a DC servomotor positioning system are given to illustrate the validity of the results developed in this paper.

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Precise friction control for the nonlinear friction system using the friction state observer and sliding mode control with recurrent fuzzy neural networks

Cited by 38 publications

References 19 publications

An Optimized RBF Neural Network Based on Beetle Antennae Search Algorithm for Modeling the Static Friction in a Robotic Manipulator Joint

An Optimized RBF Neural Network Based on Beetle Antennae Search Algorithm for Modeling the Static Friction in a Robotic Manipulator Joint

Static Friction in a Robot Joint—Modeling and Identification of Load and Temperature Effects

Tracker design using composite nonlinear feedback with application to a DC servomotor

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