Adaptive friction compensation using neural network approximations

We present a new compensation technique for a friction model, which captures problematic friction effects such as Stribeck effects, hysteresis, stick-slip limit cycling, pre-sliding displacement and rising static friction. The proposed control utilizes a PD control structure and an adaptive estimate...

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Bibliographic Details
Published inIEEE transactions on systems, man and cybernetics. Part C, Applications and reviews Vol. 30; no. 4; pp. 551 - 557
Main Authors Huang, S.N., Tan, K.K., Lee, T.H.
Format Journal Article
LanguageEnglish
Published IEEE 01.11.2000
Subjects
Adaptive control
Adaptive systems
Compensation
Computer simulation
Dynamical systems
Force control
Friction
Hysteresis
Mathematical analysis
Neural networks
PD control
Programmable control
Stability
Stability analysis
Theorems
Transient analysis
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Summary:We present a new compensation technique for a friction model, which captures problematic friction effects such as Stribeck effects, hysteresis, stick-slip limit cycling, pre-sliding displacement and rising static friction. The proposed control utilizes a PD control structure and an adaptive estimate of the friction force. Specifically, a radial basis function (RBF) is used to compensate the effects of the unknown nonlinearly occurring Stribeck parameter in the friction model. The main analytical result is a stability theorem for the proposed compensator which can achieve regional stability of the closed-loop system. Furthermore, we show that the transient performance of the resulting adaptive system is analytically quantified. To support the theoretical concepts, we present dynamic simulations for the proposed control scheme.
Bibliography:ObjectType-Article-2
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ISSN:1094-6977
1558-2442
DOI:10.1109/5326.897081