Position Control for LMCTS with Nonlinear Friction and Detent Force using DR-FNN Controller
| Authors: | Lee Jin Woo, Dong-A University, Korea, Republic of
Suh Jin Ho, Dong-A University, Korea, Republic of
Lee Young Jin, Korea Aviation Polytechnic College, Korea, Republic of
Nam Hyun Do, Dankook University, Korea, Republic of
Lee Kwon Soon, Dong-A University, Korea, Republic of |
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| Topic: | 3.2 Cognition and Control ( AI, Fuzzy, Neuro, Evolut.Comp.) |
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| Session: | Neural Networks in Modelling and Control |
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| Keywords: | Linear Motor-based Container Transfer System, position control, detent force, friction, DR-FNN |
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Abstract
This paper presents a position control strategy of the linear motor-based container transfer system (LMCTS) using the soft-computing method. LMCTS is the automatic container transporter in the port. The system has problems to control as weight changes of the mover, the nonlinear friction force, and the detent force, etc. To adapt these problems, we proposed a control system structure that was consisted of two dynamically-constructed recurrent fuzzy neural networks (DR-FNNs). These perform as a controller and a plant emulator with the same structure. And the proposed control system had better performances for the position accuracy and the amount of input consumption than the conventional PID controller and general FNNs with the fixed structure.
