Proceedings Article10.1109/ACC.2008.4587216
Passivity based iterative learning control for mechanical systems subject to dry friction
R. Quintanilla,John T. Wen +1 more
- 11 Jun 2008
- pp 4573-4578
TL;DR: A modified update law is derived based on the 2D system perspective and adjusted the combination coefficient in every iteration step to ensure passivity in the case of combined position and velocity output.
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Abstract: This paper considers iterative learning control applied to a mass-damper system subject to dry friction. The dry friction nonlinearity is discontinuous, and therefore poses challenges to the conventional learning control methods. We apply the passivity based analysis in learning control and show that it is applicable to the case with velocity output. In the case of combined position and velocity output, the passivity approach is not directly applicable. We derive a modified update law based on the 2D system perspective and adjust the combination coefficient in every iteration step to ensure passivity. Asymptotic convergence is shown under the condition that the combination coefficient does not asymptotically vanish. Simulation results are included to demonstrate the performance of these algorithms.
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Citations
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Passivity-based Iterative Learning Control Design for Selective Laser Melting
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- 27 Jun 2018
TL;DR: A control oriented reduced order model (ROM) to adequately capture temperature dynamics is proposed and validated against high fidelity FEM simulations to demonstrate the capability of the ILC algorithm to generate optimal laser power profiles for creating complicated geometries on large powder beds.
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Iterative Learning Control for Coupled Temperature and Humidity in Buildings
TL;DR: This paper proposes a model-free ILC design approach facilitated by the inherent passivity of building thermohygrometric dynamics, and first demonstrates that the building dynamics are strictly output-incremental passive, which is exploited to design ILC laws that guarantee convergence in the iteration domain, while being robust to model uncertainty.
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Monotonically convergent iterative learning control for piecewise affine systems
TL;DR: In this article, an approach to analyse monotonic convergence is developed for piecewise affine (PWA) systems by exploiting the incremental l2-gain leading to sufficient LMI conditions.
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