Adaptive friction compensation for a class of mechanical systems based on LuGre model

Chen, Pengnian; Liu, Xiangbin; Yan, Qingdong

doi:10.1002/rnc.6042

Cited by 8 publications

(2 citation statements)

References 28 publications

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“…This processing strategy has been adopted in many subsequent results, such as repetitive control for nonlinear systems with extended matching unstructured uncertainties, 21 observer based repetitive control scheme, 22 adaptive repetitive control for PMSM servo systems, 23 and adaptive friction compensation. 24 To the best of authors' knowledge, the research on robust adaptive repetitive control algorithm for PAM-driven systems has seldom been reported.…”

Section: Introductionmentioning

confidence: 99%

Design, analysis and experiment of robust adaptive repetitive angle tracking control for a one-DOF manipulator driven by pneumatic artificial muscles

Guo

Liu

Zheng

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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There exist nonlinearities, uncertainties and time-varying characteristics in the system dynamics of manipulators driven by pneumatic artificial muscles (PAMs), which makes the accurate dynamic modeling and controller design challenging. In this paper, a robust adaptive repetitive control scheme is proposed to solve the periodic-trajectory tracking problem for a one-degree of freedom (one-DOF) manipulator driven by two PAMs. First, the system model of the one-DOF PAM-driven manipulator is analyzed and derived. Next, during output constraint design by using a barrier Lyapunov function, a sliding mode surface is reasonably constructed to compensate for the differential term of time-varying constraint parameter. Then, with the help of Lipchitz condition, signal replacement technique and reparameterization are implemented to deal with nonparametric uncertainties in the manipulator system for the subsequent uncertainty compensation by using difference learning method and robust feedback strategy. Moreover, the stability of closed-loop PAM-driven manipulator is proven theoretically by using Lyapunov synthesis. In the end, comparative experiments are provided on a self-built PAM testbed to demonstrated the effectiveness of the proposed robust adaptive repetitive control scheme.

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Section: Introductionmentioning

confidence: 99%

Design, analysis and experiment of robust adaptive repetitive angle tracking control for a one-DOF manipulator driven by pneumatic artificial muscles

Guo

Liu

Zheng

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…The LuGre friction model is widely used in model-based friction compensation of control systems due to its ability to accurately describe various dynamic and static characteristics of nonlinear friction, including presliding displacement, frictional lag, and varying break-away force [5]. In LuGre-model-based friction compensation, the primary control scheme involves an adaptive control method based on an observer, which estimates the unmeasurable state variables of the LuGre model [6]. Various control methods have been proposed in this context.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Backstepping Control for Harmonic Drive System Based on Modified LuGre Friction Model

Xia,

Wang,

Song

et al. 2023

IEEE Access

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To improve the control accuracy and stability of the harmonic drive system under the influence of nonlinear friction and external disturbances, we have developed an adaptive neural backstepping control approach with friction compensation. During the design process, we employ a nonlinear observer based on a novel modified LuGre model with friction compensation. This observer effectively reduces the influence of nonlinear friction on the harmonic drive system, even under dynamic changes in the environment. Additionally, we utilize a Chebyshev neural network to approximate unknown disturbances applied to the harmonic drive system. To prevent violations of output constraints, we introduce a tangent barrier Lyapunov function. Furthermore, to address the challenges of "explosion of complexity" and poor precision associated with first-order filters in backstepping, we integrate the inverse hyperbolic sine function tracking differentiator into this controller. Finally, we employ the Lyapunov criterion to prove that all errors in the closed-loop system are uniformly bounded. Simulation results confirm the feasibility of the proposed control scheme and demonstrate better closed-loop behavior compared to that obtained using a radial basis function dynamic surface controller.INDEX TERMS Adaptive neural backstepping control, Chebyshev neural network, harmonic drive system, modified LuGre model, tangent barrier Lyapunov function.

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Contact-impact events with friction in multibody dynamics: Back to basics

Flores

Ambrósio

Lankarani

2023

Mechanism and Machine Theory

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Adaptive friction compensation for a class of mechanical systems based on LuGre model

Cited by 8 publications

References 28 publications

Design, analysis and experiment of robust adaptive repetitive angle tracking control for a one-DOF manipulator driven by pneumatic artificial muscles

Design, analysis and experiment of robust adaptive repetitive angle tracking control for a one-DOF manipulator driven by pneumatic artificial muscles

Adaptive Neural Backstepping Control for Harmonic Drive System Based on Modified LuGre Friction Model

Contact-impact events with friction in multibody dynamics: Back to basics

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