Neural network-based adaptive funnel sliding mode control for servo mechanisms with friction compensation

Shubo Wang; Qiang Chen; Xuemei Ren; Haisheng Yu

doi:10.1016/j.neucom.2019.10.006

Neural network-based adaptive funnel sliding mode control for servo mechanisms with friction compensation

Shubo Wang^*, Qiang Chen, Xuemei Ren, Haisheng Yu

^*Corresponding author for this work

School of Automation

Research output: Contribution to journal › Article › peer-review

29 Citations (Scopus)

Abstract

In this paper, a novel adaptive funnel sliding mode control scheme is proposed for servo mechanisms with friction compensation. A continuously differentiable friction model is employed to capture the unknown friction dynamics. The friction nonlinearities and unknown dynamics are estimated by using neural network (NN). Moreover, a modified funnel variable, which relaxes limitation in original funnel control (e.g., systems with relative degree 1 or 2), is developed using the tracking error to replace the scaling factor, which is used to design the sliding mode surface. Then, a novel adaptive funnel sliding mode control scheme is proposed for servo mechanisms to improve the transient performance. The effectiveness of the developed control method is validated via experimental results.

Original language	English
Pages (from-to)	16-26
Number of pages	11
Journal	Neurocomputing
Volume	377
DOIs	https://doi.org/10.1016/j.neucom.2019.10.006
Publication status	Published - 15 Feb 2020

Keywords

Adaptive control
Funnel function
Neural network (NN)
Servo mechanism
Sliding mode control (SMC)

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10.1016/j.neucom.2019.10.006

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Wang, S., Chen, Q., Ren, X., & Yu, H. (2020). Neural network-based adaptive funnel sliding mode control for servo mechanisms with friction compensation. Neurocomputing, 377, 16-26. https://doi.org/10.1016/j.neucom.2019.10.006

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abstract = "In this paper, a novel adaptive funnel sliding mode control scheme is proposed for servo mechanisms with friction compensation. A continuously differentiable friction model is employed to capture the unknown friction dynamics. The friction nonlinearities and unknown dynamics are estimated by using neural network (NN). Moreover, a modified funnel variable, which relaxes limitation in original funnel control (e.g., systems with relative degree 1 or 2), is developed using the tracking error to replace the scaling factor, which is used to design the sliding mode surface. Then, a novel adaptive funnel sliding mode control scheme is proposed for servo mechanisms to improve the transient performance. The effectiveness of the developed control method is validated via experimental results.",

keywords = "Adaptive control, Funnel function, Neural network (NN), Servo mechanism, Sliding mode control (SMC)",

author = "Shubo Wang and Qiang Chen and Xuemei Ren and Haisheng Yu",

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AU - Ren, Xuemei

AU - Yu, Haisheng

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Y1 - 2020/2/15

N2 - In this paper, a novel adaptive funnel sliding mode control scheme is proposed for servo mechanisms with friction compensation. A continuously differentiable friction model is employed to capture the unknown friction dynamics. The friction nonlinearities and unknown dynamics are estimated by using neural network (NN). Moreover, a modified funnel variable, which relaxes limitation in original funnel control (e.g., systems with relative degree 1 or 2), is developed using the tracking error to replace the scaling factor, which is used to design the sliding mode surface. Then, a novel adaptive funnel sliding mode control scheme is proposed for servo mechanisms to improve the transient performance. The effectiveness of the developed control method is validated via experimental results.

AB - In this paper, a novel adaptive funnel sliding mode control scheme is proposed for servo mechanisms with friction compensation. A continuously differentiable friction model is employed to capture the unknown friction dynamics. The friction nonlinearities and unknown dynamics are estimated by using neural network (NN). Moreover, a modified funnel variable, which relaxes limitation in original funnel control (e.g., systems with relative degree 1 or 2), is developed using the tracking error to replace the scaling factor, which is used to design the sliding mode surface. Then, a novel adaptive funnel sliding mode control scheme is proposed for servo mechanisms to improve the transient performance. The effectiveness of the developed control method is validated via experimental results.

KW - Adaptive control

KW - Funnel function

KW - Neural network (NN)

KW - Servo mechanism

KW - Sliding mode control (SMC)

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VL - 377

SP - 16

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JO - Neurocomputing

JF - Neurocomputing

ER -

Neural network-based adaptive funnel sliding mode control for servo mechanisms with friction compensation

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Keywords

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