Design an Intelligent Neural-Fuzzy Controller for Hybrid Motorcycle

The main propose of this article is to design an intelligent neural-fuzzy controller for hybrid motorcycle. A self-tuning PID tracking controller based on RBF neural network with Fuzzy current limiter is proposed to maneuver the motor and save some energy in hybrid mode. The outer motor control loop...

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Bibliographische Detailangaben
Hauptverfasser:	Yao-Lun Liu, Chia-Chang Tong, Wu-Shun Jwo, Shuen-Jeng Lin
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Current limiters Fluctuations Fuzzy Fuzzy control Fuzzy neural networks Hybrid Vehicle Motor drives Motorcycles Neural networks Power demand Radial Basis Function (RBF) Neural Network Self-tuning PID Controller Three-term control Tracking loops
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Beschreibung
Zusammenfassung:	The main propose of this article is to design an intelligent neural-fuzzy controller for hybrid motorcycle. A self-tuning PID tracking controller based on RBF neural network with Fuzzy current limiter is proposed to maneuver the motor and save some energy in hybrid mode. The outer motor control loop is designed to track down the speed fluctuations by Neural-PID controller. Besides, one inner loop is designed to limit the armature current whenever the power demand is diminished according to a set of Fuzzy rules. The proposed structure is put into tests by Matlab programming. Simulations confirm this RBF-PID controller with Fuzzy current limiter can save 23.5% energy for a tracking task.
DOI:	10.1109/NAFIPS.2007.383852