2007 46th IEEE Conference on Decision and Control 2007
DOI: 10.1109/cdc.2007.4434037
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Adaptive compensation of modeled friction using a RBF neural network approximation

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… Show more

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Cited by 9 publications
(4 citation statements)
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References 23 publications
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“…The predicted torque is calculated using Equation ( 5). When it comes to the test set, a different excitation trajectory can be generated by adding a new constraint based on Equation (6). To apply a new constraint q i (t = 3.8) = 0, the test set trajectory is shown in Figure 4.…”
Section: Excitation Trajectory Generationmentioning
confidence: 99%
See 1 more Smart Citation
“…The predicted torque is calculated using Equation ( 5). When it comes to the test set, a different excitation trajectory can be generated by adding a new constraint based on Equation (6). To apply a new constraint q i (t = 3.8) = 0, the test set trajectory is shown in Figure 4.…”
Section: Excitation Trajectory Generationmentioning
confidence: 99%
“…Many researchers have proposed different error compensation policies [4,5]. Machine learning methods, such as back propagation (BP) neural network [6] and radial basis function (RBF) network [7,8], are also used to fix or compensate errors. C Li et al [9] present a novel dynamic modeling method by using a recurrent neural network (RNN) with an incomplete state system variables observation.…”
Section: Introductionmentioning
confidence: 99%
“…Using this property, NNs are used to build compensators for the friction models. [42][43][44] NNs are also used to handle the unknown dynamics including friction discontinuity. 30 However, the approximation error exists and depends on the structure of the NN.…”
Section: Related Workmentioning
confidence: 99%
“…Neural networks have the ability to approximate nonlinear function. As in [5], RBF network was used to approximate the parameters for nonlinear characteristics of friction. Therefore, RBF network is reasonably suitable to be used in nonlinear switching law design.…”
Section: Smc Control Law Designmentioning
confidence: 99%