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|Title:||Adaptive friction compensation using neural network approximations||Authors:||Huang, S.N.
|Issue Date:||Nov-2000||Citation:||Huang, S.N., Tan, K.K., Lee, T.H. (2000-11). Adaptive friction compensation using neural network approximations. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews 30 (4) : 551-557. ScholarBank@NUS Repository. https://doi.org/10.1109/5326.897081||Abstract:||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.||Source Title:||IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews||URI:||http://scholarbank.nus.edu.sg/handle/10635/61751||ISSN:||10946977||DOI:||10.1109/5326.897081|
|Appears in Collections:||Staff Publications|
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