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IEEE 7th Data Driven control and learning Systems Conference (DDCLS)

作者： Jian, Yupei Kang, Xin Yang, Wanqiu Min, Da Huang, Deqing Southwest Jiaotong Univ Sch Elect Engn Chengdu 610097 Sichuan Peoples R China

ISBN: (纸本)9781538626184

Suffering from rate-dependent hysteretic nonlinearity, accurate positioning of nanopositioning stage driven by piezoelectric actuators(PEAs) is hard to achieve and effective controllers are urgently needed. In this paper, by assuming that the model of PEA takes a Hammerstein structure, a dual-loop iterative learning control (ILC) scheme is designed to deal with both input hysteresis and linear dynamics of system synchronously. The merit of this controller lies in that, accurate system models are not prerequisite so that the control process is greatly simplified. As a comparison, an extra experiment using single loop ILC is performed to manifest the efficacy of the proposed algorithm. Simulation results show that the dual-loop ILC scheme is superior to the single loop ILC scheme in terms of convergence speed and control accuracy.

关键词： piezoelectric actuator tracking precision dual-loop iterative learning control hysteresis

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A dual-loop iterative learning control for Nonlinear Systems with Hysteresis Input Uncertainty

A Dual-loop Iterative Learning Control for Nonlinear Systems...

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IEEE International Conference on control and Automation

作者： Huang, Deqing Tan, Ying Xu, Jian-Xin Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia

ISBN: (纸本)9781424447060

In this work, a dual-loop iterative learning control (ILC) scheme is designed for a class of nonlinear systems with hysteresis input uncertainty. The two ILC loops are applied to the nominal part and the hysteresis part respectively, to learn their unknown dynamics. Based on the convergence analysis for each single loop, a composite energy function method is then adopted to prove the learning convergence of the dual-loop system in iteration domain. One illustrative example shows that the proposed dual learning control scheme can work well under hysteresis input uncertainty.

关键词： Hysteresis Input uncertainty dual-loop iterative learning control Composite energy function

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Unified iterative learning control schemes for nonlinear dynamic systems with nonlinear input uncertainties

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AUTOMATICA 2012年第12期48卷 3173-3182页

作者： Tan, Ying Dai, Hao-Hui Huang, Deqing Xu, Jian-Xin Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia E China Normal Univ Dept Math Shanghai 200241 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore

In many ILC algorithms, nonlinear input uncertainties such as saturation, dead-zone and hysteresis, which do exist due to practical implementations, are always ignored. Although various ILC algorithms have been proposed to compensate various nonlinear input uncertainties, a systematic design framework is still missing. This note presents a unified design framework to deal with very general nonlinear input uncertainties. The concept of a dual-loop ILC is introduced. One ILC loop (ILC loop 1) is designed for the nominal model without nonlinear input uncertainties. The other ILC loop (ILC loop 2) uses some iterative algorithms to handle nonlinear input uncertainties. Two ILC loops can be designed independently and are connected by a proper time-scale separation. Our first result shows that by using time-scale separation, the overall system semi-globally practically converges to the desired trajectory if ILC loop 2 uniformly converges. Furthermore, if ILC loop 2 converges "almost" monotonically, ILC loop 1 and ILC loop 2 can update simultaneously to achieve uniform convergence of the overall system. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： dual-loop iterative learning control Nonlinear input uncertainties Inter-connections

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