Friction compensation using adaptive non-linear control with persistent excitation
TLDR
In this article, an adaptive non-linear friction compensation scheme for a friction model is presented, which captures problematic friction effects such as Stribeck effect, hysteresis, stick-slip limit cycling, pre-sliding displacement and rising static friction.Abstract:
Non-linear frictional dynamics reduce the tracking performance of machine control systems involving high-precision, low-velocity tasks. We present an adaptive non-linear friction compensation scheme for a friction model, which captures problematic friction effects such as Stribeck effect, hysteresis, stick-slip limit cycling, pre-sliding displacement and rising static friction. We show that without robust adaptation, frictional dynamics and other modelling uncertainties can cause an adaptive friction compensation scheme to become unstable. We extend robust adaptive theory to include a new type of error model with a non-linear regression vector and Lipschitz disturbances. By using persistent excitation in the desired trajectory, our controller achieves stable adaptation for friction force effects due to static, Coulomb and viscous components, as well as for inertia and the Stribeck effects, while remaining robust to perturbations in friction force due to frictional lag and frictional memory. Although the S...read more
Citations
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Adaptive backstepping control and friction compensation for AC servo with inertia and load uncertainties
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TL;DR: An adaptive backstepping control with friction compensation scheme is presented and system robustness and asymptotic position tracking performance are shown through simulation and experimental results.
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Intelligent Friction Modeling and Compensation Using Neural Network Approximations
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TL;DR: Two neural networks (NNs) are employed in the proposed intelligent controller due to the learning capability of the NNs, which can compensate the effects of the nonlinear friction.
Book ChapterDOI
Adaptive friction compensation of servo mechanisms
TL;DR: In this article, adaptive friction compensation using both model-based and neural network (non-model-based) parameterization techniques is investigated, and extensive computer simulations are carried out to show the effectiveness of the proposed control techniques, and illustrate the effects of certain system parameters on the performance of the closed-loop system.
Journal ArticleDOI
Comparison of EKBF-based and Classical Friction Compensation
TL;DR: In this article, an extended Kalman-Bucy filter (EKBF)-based approach that does not use a phenomenological or structured model for friction has been proposed, and the EKBF can also be used to provide parameter adaptation for simple friction models.