We present a compensation technique for a friction model, which captures problematic friction effects such as Stribeck effect, hysteresis, pre-sliding displacement, stick-slip motion and stiction. 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 non-linear friction model. The asymptotic convergence of parameter estimation errors is achieved for the system in adaptive observer form using Barbalat's Lemma. We also introduce a parameter estimation projection algorithm to avoid the parameter estimates drift when the condition of persistency of excitation is not verified. Finally, to support the theoretical concepts, we present dynamic simulations for the proposed control scheme.

Vitiello, V., Tornambe', A. (2007). Adaptive compensation of modeled friction using a RBF neural network approximation. In Proceedings of the IEEE conference on decision and control (pp.197-202). New York : IEEE [10.1109/CDC.2007.4434037].

Adaptive compensation of modeled friction using a RBF neural network approximation

TORNAMBE', ANTONIO
2007-01-01

Abstract

We present a compensation technique for a friction model, which captures problematic friction effects such as Stribeck effect, hysteresis, pre-sliding displacement, stick-slip motion and stiction. 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 non-linear friction model. The asymptotic convergence of parameter estimation errors is achieved for the system in adaptive observer form using Barbalat's Lemma. We also introduce a parameter estimation projection algorithm to avoid the parameter estimates drift when the condition of persistency of excitation is not verified. Finally, to support the theoretical concepts, we present dynamic simulations for the proposed control scheme.
IEEE Conference on decision and control
New Orleans
2007
46.
Rilevanza internazionale
2007
Settore INF/01 - INFORMATICA
English
adaptive systems; hysteresis; mathematical models; problem solving; radial basis function networks; two term control systems; pre-sliding displacement; stickslip motion; stribeck effect; friction
Intervento a convegno
Vitiello, V., Tornambe', A. (2007). Adaptive compensation of modeled friction using a RBF neural network approximation. In Proceedings of the IEEE conference on decision and control (pp.197-202). New York : IEEE [10.1109/CDC.2007.4434037].
Vitiello, V; Tornambe', A
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/7885
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