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Please use this identifier to cite or link to this item: http://nchuir.lib.nchu.edu.tw/handle/309270000/154851

標題: 電動兩輪車之系統設計與控制
System Design and Control of an Electric Two-Wheeled Robotic Vehicle
作者: 蔡青航
Tsai, Ching-Hang
Contributors: 蔡清池
電機工程學系所
關鍵字: 電動兩輪車;模糊小波網路;非線性
fuzzy wavelet neural network;electric two-wheeled robotic vehicle;nonlinear
日期: 2013
Issue Date: 2013-11-21 11:21:12 (UTC+8)
Publisher: 電機工程學系所
摘要: 本論文旨在針對一電動兩輪車,發展兩種智慧適應運動控制策略及其嵌入式控制器研製技術。藉由已發展的非線性數學模型,兩種結合模糊小波網路(FWNN)之智慧適應型運動控制策略被提出,用以達成自平衡,速度追蹤與轉向角速度控制。第一種智慧適應型運動控制方法使用模糊小波網路近似學習模型不確定與時變的摩擦力,而第二種智慧適應型運動控制方法則採用模糊小波網路直接學習該非線性控制器的能力。電腦模擬結果證實本文所建立的兩種控制器優於現有使用模糊基礎函數的兩種控制器。本文使用德儀TMS320F28335數位訊號控制器製作一台嵌入式控制器,用以實現所提的兩種智慧適應型運動控制策略。實驗結果證實本文所提的控制器之實用性。
This thesis develops techniques for intelligent adaptive motion control and embedded implementation of an electric two-wheeled robotic vehicle using fuzzy wavelet neural networks (FWNN). With the nonlinear model of the vehicle, two kinds of intelligent adaptive motion controllers using FWNN are respectively proposed to achieve self-balancing, speed tracking and yaw motion control. The first intelligent adaptive controller aims to use FWNN to on-line learn the un-modeling errors and unknown or terrain-dependent frictions approximately, whereas the second one adopts FWNN to on-line mimic the function of the nonlinear controllers and works as a main controller. Simulation results are performed to verify the effectiveness and merits of the proposed intelligent adaptive motion controllers and both proposed controllers are shown to outperform existing controllers using fuzzy basis-function networks (FBFN). Both proposed controllers have been implemented into a digital signal controller using the single-chip DSP, TMS320F28335, from Texas Instrument. Experimental results confirm the applicability of the proposed controllers.
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