電機工程學系

Permanent URI for this communityhttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/85

歷史沿革

本系成立宗旨在整合電子、電機、資訊、控制等多學門之工程技術,以培養跨領域具系統整合能力之電機電子科技人才為目標,同時配合產業界需求、支援國家重點科技發展,以「系統晶片」、「多媒體與通訊」、與「智慧型控制與機器人」等三大領域為核心發展方向,期望藉由學術創新引領產業發展,全力培養能直接投入電機電子產業之高級技術人才,厚植本國科技產業之競爭實力。

本系肇始於民國92年籌設之「應用電子科技研究所」,經一年籌劃,於民國93年8月正式成立,開始招收碩士班研究生,以培養具備理論、實務能力之高階電機電子科技人才為目標。民國96年8月「應用電子科技學系」成立,招收學士班學生,同時間,系所合一為「應用電子科技學系」。民國103年8月更名為「電機工程學系」,民國107年電機工程學系博士班成立,完備從大學部到博士班之學制規模,進一步擴展與深化本系的教學與研究能量。

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    An online GA-based output-feedback direct adaptive fuzzy-neural controller for uncertain nonlinear systems
    (IEEE Systems, Man, and Cybernetics Society, 2004-02-01) W.-Y. Wang; C.-Y. Cheng; Y.-G. Leu
    In this paper, we propose a novel design of a GA-based output-feedback direct adaptive fuzzy-neural controller (GODAF controller) for uncertain nonlinear dynamical systems. The weighting factors of the direct adaptive fuzzy-neural controller can successfully be tuned online via a GA approach. Because of the capability of genetic algorithms (GAs) in directed random search for global optimization, one is used to evolutionarily obtain the optimal weighting factors for the fuzzy-neural network. Specifically, we use a reduced-form genetic algorithm (RGA) to adjust the weightings of the fuzzy-neural network. In RGA, a sequential-search -based crossover point (SSCP) method determines a suitable crossover point before a single gene crossover actually takes place so that the speed of searching for an optimal weighting vector of the fuzzy-neural network can be improved. A new fitness function for online tuning the weighting vector of the fuzzy-neural controller is established by the Lyapunov design approach. A supervisory controller is incorporated into the GODAF controller to guarantee the stability of the closed-loop nonlinear system. Examples of nonlinear systems controlled by the GODAF controller are demonstrated to illustrate the effectiveness of the proposed method.