Three-Degree-of-Freedom Dynamic Model Based IT2RFNN Control for Gantry Position Stage
A three-degree-of-freedom (3-DOF) dynamic model based interval type-2 recurrent fuzzy neural network (IT2RFNN) control system is proposed in this study for a gantry position stage with three permanent magnet linear synchronous motors (PMLSMs).In general,the gantry position stages are controlled using independent axis control without considering the effect of inter-axis mechanical coupling.To consider the effect of inter-axis mechanical coupling which degenerates control performance and leads to synchronous error,a Lagrangian equation based 3-DOF dynamic model for gantry position stage is derived first.Then,in order to minimize the synchronous error and tracking error of the gantry position stage,the 3-DOF dynamic model based IT2RFNN control system is proposed.In this approach,the IT2RFNN,which combines the advantages of interval type-2 fuzzy logic system (FLS) and recurrent neural network,is developed to approximate an unknown dynamic function.Moreover,adaptive learning algorithms which can train the parameters of the IT2RFNN on-line are derived from the Lyapunov stability theorem.Finally,some experimental results of optical inspection application are illustrated to show the validity of the proposed control approach.
Po-Huan Chou Faa-Jeng Lin Chin-Sheng Chen Feng-Chi Lee Ying-Min Chen
Department of Mechatronics Control, Industrial Technology Research Institute, Taiwan Department of Electrical Engineering, National Central University, Taiwan Graduate Institute of Automation Technology, National Taipei University of Technology, Taiwan
国际会议
杭州
英文
1-6
2013-07-07（万方平台首次上网日期，不代表论文的发表时间）