Computed force control system using functional link radial basis function network with asymmetric membership function for piezo-flexural nanopositioning stage

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Title:	Computed force control system using functional link radial basis function network with asymmetric membership function for piezo-flexural nanopositioning stage
Authors:	林法正;Lin, Faa-Jeng;Lee, Shih-Yang;Chou, Po-Huan
Contributors:	資訊電機學院電機工程學系
Keywords:	adaptive control;adaptive learning algorithms;asymmetric membership function;auxiliary control;Computation;computed force control system;dynamic characteristics;equivalent hysteresis friction force;FLRBFN‐AMF;force control;functional link radial basis function network;fuzzy rules;fuzzy set theory;improved steady‐state response;learning systems;lumped uncertainty;Lyapunov methods;Lyapunov stability theorem;motion control;nanopositioning;neurocontrollers;nonlinear control systems;nonlinear system;PFNS mechanism;piezo‐flexural nanopositioning stage;radial basis function networks;reference contours;reference trajectories;stability;three‐dimension motion control;time varying system;time‐varying systems
Date:	2013-11-25
Issue Date:	2026-04-23 13:43:44 (UTC+8)
Publisher:	Institution of Engineering and Technology;Stevenage: The Institution of Engineering and Technology
Abstract:	摘要： A computed force control system using functional link radial basis function network with asymmetric membership function (FLRBFN-AMF) for three-dimension motion control of a piezo-flexural nanopositioning stage (PFNS) is proposed in this study. First, the dynamics of the PFNS mechanism with the introduction of a lumped uncertainty including the equivalent hysteresis friction force are derived. Then, a computed force control system with an auxiliary control is proposed for the tracking of the reference contours with improved steady-state response. Since the dynamic characteristics of the PFNS are non-linear and time varying, a computed force control system using FLRBFN-AMF is designed to improve the control performance for the tracking of various reference trajectories, where the FLRBFN-AMF is employed to estimate a non-linear function including the lumped uncertainty of the PFNS. Moreover, by using the asymmetric membership function, the learning capability of the networks can be upgraded and the number of fuzzy rules can be optimised for the functional link radial basis function network. Furthermore, the adaptive learning algorithms for the training of the parameters of the FLRBFN-AMF online are derived using the Lyapunov stability theorem. Finally, some experimental results for the tracking of various reference contours of the PFNS are given to demonstrate the validity of the proposed control system. 出版者： Stevenage: The Institution of Engineering and Technology 出版日期： 2013-12 出處： IET control theory & applications, 2013-12, Vol.7 (18), p.2128-2142 資源來源： Wiley Online Library Open Access 版權： The Institution of Engineering and Technology 版權： 2021 The Authors. IET Control Theory & Applications published by John Wiley & Sons, Ltd. on behalf of The Institution of Engineering and Technology 版權： Copyright The Institution of Engineering & Technology Dec 2013 識別號： ISSN: 1751-8644 識別號： ISSN: 1751-8652 識別號： EISSN: 1751-8652 識別號： DOI: 10.1049/iet-cta.2013.0086
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

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