A new dynamical linearization based adaptive ILC for nonlinear discrete-time MIMO systems

Most of the available results of adaptive iterative learning control (AILC) hitherto have considered the control systems with known linearly parameterized structures. A dynamical linearization approach is developed for a general nonlinear multiple input multiple output systems. And then a discrete-t...

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Main Authors: Chi, Ronghu, Hou, Zhongsheng, Jin, Shangtai, Wang, Danwei
Other Authors: School of Electrical and Electronic Engineering
Format: Conference Paper
Language:English
Published: 2013
Subjects:
Online Access:https://hdl.handle.net/10356/97006
http://hdl.handle.net/10220/11801
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author Chi, Ronghu
Hou, Zhongsheng
Jin, Shangtai
Wang, Danwei
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Chi, Ronghu
Hou, Zhongsheng
Jin, Shangtai
Wang, Danwei
author_sort Chi, Ronghu
collection NTU
description Most of the available results of adaptive iterative learning control (AILC) hitherto have considered the control systems with known linearly parameterized structures. A dynamical linearization approach is developed for a general nonlinear multiple input multiple output systems. And then a discrete-time adaptive ILC approach is presented to deal with the ILC problems of nonlinear MIMO systems with iteration-varying initial error and reference trajectory. The controller design and analysis is completely data-driven without using any modeling information of the plant, but the measured I/O data only. The almost perfect tracking performance is asymptotically guaranteed by rigirous mathematical analysis.
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spelling ntu-10356/970062020-03-07T13:24:47Z A new dynamical linearization based adaptive ILC for nonlinear discrete-time MIMO systems Chi, Ronghu Hou, Zhongsheng Jin, Shangtai Wang, Danwei School of Electrical and Electronic Engineering International Conference on Control Automation Robotics & Vision (12th : 2012 : Guangzhou, China) DRNTU::Engineering::Electrical and electronic engineering Most of the available results of adaptive iterative learning control (AILC) hitherto have considered the control systems with known linearly parameterized structures. A dynamical linearization approach is developed for a general nonlinear multiple input multiple output systems. And then a discrete-time adaptive ILC approach is presented to deal with the ILC problems of nonlinear MIMO systems with iteration-varying initial error and reference trajectory. The controller design and analysis is completely data-driven without using any modeling information of the plant, but the measured I/O data only. The almost perfect tracking performance is asymptotically guaranteed by rigirous mathematical analysis. 2013-07-18T01:14:08Z 2019-12-06T19:37:50Z 2013-07-18T01:14:08Z 2019-12-06T19:37:50Z 2012 2012 Conference Paper Chi, R., Hou, Z., Jin, S.,& Wang, D. (2012). A new dynamical linearization based adaptive ILC for nonlinear discrete-time MIMO systems. 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV), 442-446. https://hdl.handle.net/10356/97006 http://hdl.handle.net/10220/11801 10.1109/ICARCV.2012.6485199 en © 2012 IEEE.
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Chi, Ronghu
Hou, Zhongsheng
Jin, Shangtai
Wang, Danwei
A new dynamical linearization based adaptive ILC for nonlinear discrete-time MIMO systems
title A new dynamical linearization based adaptive ILC for nonlinear discrete-time MIMO systems
title_full A new dynamical linearization based adaptive ILC for nonlinear discrete-time MIMO systems
title_fullStr A new dynamical linearization based adaptive ILC for nonlinear discrete-time MIMO systems
title_full_unstemmed A new dynamical linearization based adaptive ILC for nonlinear discrete-time MIMO systems
title_short A new dynamical linearization based adaptive ILC for nonlinear discrete-time MIMO systems
title_sort new dynamical linearization based adaptive ilc for nonlinear discrete time mimo systems
topic DRNTU::Engineering::Electrical and electronic engineering
url https://hdl.handle.net/10356/97006
http://hdl.handle.net/10220/11801
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