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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    以DSP基礎建立即時模糊類神經網路之研究
    (行政院國家科學委員會, 1998-07-31) 王偉彥
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    H-inf.-observer-based adaptive fuzzy-neural control for a class of uncertain nonlinear systems
    (1999-10-15) Y.-G. Leu; W.-Y. Wang; T.-T. Lee
    This paper presents a method for designing an H∞-observer-based adaptive fuzzy-neural output feedback control law with on-line tuning of fuzzy-neural weighting factors for a class of uncertain nonlinear systems based on the H∞ control technique and the strictly positive real Lyapunov (SPR-Lyapunov) design approach. The H∞-observer-based output feedback control law guarantees that all signals involved are bounded and provides the modeling error (and the external bounded disturbance) attenuation with H∞ performance, obtained by a Riccati-Like equation. Besides, the H∞-observer-based output feedback control law doesn't require the assumptions of the total system states available for measurement and the uncertain system nonlinearities only restricted to the system output. Finally, an example is simulated in order to confirm the effectiveness and applicability of the proposed methods
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    Observer-based adaptive fuzzy-neural control for unknown nonlinear dynamical systems
    (IEEE Systems, Man, and Cybernetics Society, 1999-10-01) Y.-G. Leu; T.-T. Lee; W.-Y. Wang
    In this paper, an observer-based adaptive fuzzy-neural controller for a class of unknown nonlinear dynamical systems is developed. The observer-based output feedback control law and update law to tune on-line the weighting factors of the adaptive fuzzy-neural controller are derived. The total states of the nonlinear system are not assumed to be available for measurement. Also, the unknown nonlinearities of the nonlinear dynamical systems are not restricted to the system output only. The overall adaptive scheme guarantees that all signals involved are bounded. Simulation results demonstrate the applicability of the proposed method in order to achieve desired performance