科技與工程學院

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沿革

科技與工程學院（原名為科技學院）於87學年度成立，其目標除致力於科技與工程教育師資培育外，亦積極培育與科技產業有關之工程及管理專業人才。學院成立之初在原有之工業教育學系、工業科技教育學系、圖文傳播學系等三系下，自91學年度增設「機電科技研究所」，該所於93學年度起設立學士班並更名為「機電科技學系」。本學院於93學年度亦增設「應用電子科技研究所」，並於96學年度合併工教系電機電子組成立「應用電子科技學系」。此外，「工業科技教育學系」於98學年度更名為「科技應用與人力資源發展學系」朝向培育科技產業之人力資源專才。之後，本院為配合本校轉型之規劃，增加學生於科技與工程產業職場的競爭，本院之「機電科技學系」與「應用電子科技學系」逐漸朝工程技術發展，兩系並於103學年度起分別更名為「機電工程學系」及「電機工程學系」。同年，本學院名稱亦由原「科技學院」更名為「科技與工程學院」。至此，本院發展之重點涵蓋教育（技職教育/科技教育/工程教育）、科技及工程等三大領域，並定位為以技術為本位之應用型學院。

107學年度，為配合本校轉型規劃，「光電科技研究所」由原隸屬於理學院改為隸屬本（科技與工程）學院，另增設2學程，分別為「車輛與能源工程學士學位學程」及「光電工程學士學位學程」。

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學院網址：http://www.cot.ntnu.edu.tw/

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Author: search.filters.author.T.-T. LeeSubject: search.filters.subject.Adaptive control

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Now showing 1 - 3 of 3
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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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    H-inf. tracking-based sliding mode control for uncertain nonlinear systems via an adaptive fuzzy-neural approach
    (IEEE Systems, Man, and Cybernetics Society, 2002-08-01) W.-Y. Wang; M.-L. Chan; C.-C. James Hsu; T.-T. Lee
    A novel adaptive fuzzy-neural sliding-mode controller with H∞ tracking performance for uncertain nonlinear systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbances and approximate errors. Because of the advantages of fuzzy-neural systems, which can uniformly approximate nonlinear continuous functions to arbitrary accuracy, adaptive fuzzy-neural control theory is then employed to derive the update laws for approximating the uncertain nonlinear functions of the dynamical system. Furthermore, the H∞ tracking design technique and the sliding-mode control method are incorporated into the adaptive fuzzy-neural control scheme so that the derived controller is robust with respect to unmodeled dynamics, disturbances and approximate errors. Compared with conventional methods, the proposed approach not only assures closed-loop stability, but also guarantees an H∞ tracking performance for the overall system based on a much relaxed assumption without prior knowledge on the upper bound of the lumped uncertainties. Simulation results have demonstrated that the effect of the lumped uncertainties on tracking error is efficiently attenuated, and chattering of the control input is significantly reduced by using the proposed approach
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    Robust adaptive fuzzy-neural controllers for uncertain nonlinear systems
    (IEEE Robotics and Automation Society, 1999-10-01) Y.-G. Leu; W.-Y. Wang; T.-T. Lee
    A robust adaptive fuzzy-neural controller for a class of unknown nonlinear dynamic systems with external disturbances is proposed. The fuzzy-neural approximator is established to approximate an unknown nonlinear dynamic system in a linearized way. The fuzzy B-spline membership function (BMF) which possesses a fixed number of control points is developed for online tuning. The concept of tuning the adjustable vectors, which include membership functions and weighting factors, is described to derive the update laws of the robust adaptive fuzzy-neural controller. Furthermore, the effect of all the unmodeled dynamics, BMF modeling errors and external disturbances on the tracking error is attenuated by the error compensator which is also constructed by fuzzy-neural inference. We prove that the closed-loop system which is controlled by the robust adaptive fuzzy-neural controller is stable and the tracking error will converge to zero under mild assumptions. Several examples are simulated in order to confirm the effectiveness and applicability of the proposed methods