Recurrent Neuro-Fuzzy Modeling and Fuzzy MDPP Control for Flexible Servomechanisms |
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| Authors: | Chorng-Shyan Lin Tachung Yang Yeong-Chau Jou Lih-Chang Lin |
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| Affiliation: | (1) Chung-Shan Institute of Science and Technology, Taiwan;(2) Department of Mechanical Engineering, Yuan-Ze University, Taiwan;(3) Department of Mechanical Engineering, National Chung Hsing University, Taiwan, R.O.C |
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| Abstract: | This paper considers the nonlinear system identification and control for flexible servomechanisms. A multi-step-ahead recurrent neuro-fuzzy model consisting of local linear ARMA (autoregressive moving average) models with bias terms is suggested for approximating the dynamic behavior of a servomechanism including the effects of flexibility and friction. The RLS (recursive least squares) algorithm is adopted for obtaining the optimal consequent parameters of the rules. Within each fuzzy operating region, a local MDPP (minimum degree pole placement) control law with integral action can be constructed based on the estimated local model. Then a fuzzy controller composed of these local MDPP controls can be easily constructed for the servomechanism. The techniques are illustrated using computer simulations. |
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| Keywords: | recurrent neuro-fuzzy model TS fuzzy model RLS algorithm fuzzy MDPP control servomechanism flexibility friction |
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