Design an Intelligent Neural-Fuzzy Controller for Hybrid Motorcycle

Yao-Lun Liu, Chia-Chang Tong, Wu-Shun Jwo, Shuen-Jeng Lin
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引用次数: 16

Abstract

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.
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混合动力摩托车智能神经模糊控制器设计
本文的主要内容是为混合动力摩托车设计一种智能神经模糊控制器。提出了一种基于模糊限流器的RBF神经网络自整定PID跟踪控制器,以实现混合模式下电机的机动和节能。电机外部控制回路采用神经pid控制器跟踪转速波动。此外,还设计了一个内环,根据一组模糊规则,在电力需求减少时限制电枢电流。通过Matlab编程对该结构进行了测试。仿真结果表明,该带模糊限流器的RBF-PID控制器可为跟踪任务节省23.5%的能量。
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