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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Item 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. LeeThis 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 methodsItem A composite controller for unknown nonlinear dynamical systems using robust adaptive fuzzy-neural control schemes(2000-09-27) W.-Y. Wang; C.-C. Hsu; Y.-G. LeuA robust adaptive fuzzy-neural control scheme for nonlinear dynamical systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbance and modeling errors. A composite update law, which has a generalized form combining the projection algorithm modification and the switching-σ adaptive law, is used to tune the adjustable parameters for preventing parameter drift and confining states of the system into the specified regions. Moreover, a fuzzy variable structure control method is incorporated into the control law so that the derived controller is robust with respect to unmodeled dynamics, disturbances and modeling errors. Compared with previous control schemes for nonlinear systems, the magnitude of the control input by using the proposed approach is much smaller, which is a significant advantage in designing controllers for practical applications. To demonstrate the effectiveness and applicability of the proposed method, several examples are illustrated in the paperItem GA-based fuzzy-neural sliding mode controllers of robot manipulators(2004-01-01) P.-Z. Lin; W.-Y. Wang; T.-T. Lee; Y.-G. LeuItem T-S Fuzzy-Neural Control for Robot Manipulators(2008-08-25) W.-Y. Wang; Y.-H. Chien; Y.-G. Leu; Z.-H. Lee; T.-T. LeeThis paper proposes a novel method of on-line modeling and control through the Takagi-Sugeno (T-S) fuzzy-neural model for a class of general n-link robot manipulators. Compared with the previous method, the main contribution of this paper is an investigation of the more general robot systems using on-line adaptive T-S fuzzy-neural controller. Specifically, the general robot systems are exactly formed a linearized system via the mean value theorem, and then the T-S fuzzy-neural model can approximate the linearized system. Also, we propose an on-line identification algorithm and put significant emphasis on robust tracking controller design using an adaptive scheme for the robot systems. Finally, an example including two cases is provided to demonstrate feasibility and robustness of the proposed method.Item B-spline-based Adaptive Control for a Class of Nonlinear Systems(2008-06-07) Z.-H. Lee; W.-Y. Wang; Y.-G. Leu; J.-H. Yang本文提供一個以B-spline 為基礎之直接適應性控制方法來控制一個不穩定的未知非線性系統,其設計控制器的主要理論為直接式的適應性控制方法,利用B-spline 數學函數當作設計控制器的基底函數,來建構適應性控制輸入。最後透過matlab 軟體模擬結果證明控制器的設計為有效的。Item Adaptive Backstepping Control of Nonlinear Systems Using B-spline Neural Networks(2008-01-01) C.-Y. Chen; J.-Y. Lin; Z.-H. Lee; Y.-G. Leu; W.-Y. WangItem B-spline Adaptive Backstepping Controllers for Affine Nonlinear Systems(2009-06-01) Z.-H. Li; W.-Y. Wang; Y.-G. LeuItem Adaptive Backstepping Interval Type-2 Fuzzy Neural Network Controller Design for Nonaffine Nonlinear Systems(2009-12-19) C.-K. Chen; W.-Y. Wang; Y.-G. LeuItem Compact Ant Colony Optimization Algorithm Based Fuzzy Neural Network Backstepping Controller for MIMO Nonlinear Systems(2010-07-03) W.-Y. Wang; C.-K. Chen; Y.-G. Leu; C.-Y. ChenIn this paper, a compact ant colony algorithm used to tune parameters of fuzzy-neural networks is proposed for function approximation and adaptive control of nonlinear systems. In adaptive control procedure for nonlinear systems, weights of the fuzzy neural controller are online adjusted by the compact ant algorithm in order to generate appropriate control input. For the purpose of evaluating the stability of the closed-loop systems, an energy fitness function is used in the ant algorithm. Finally, a computer simulation example demonstrates the feasibility and effectiveness of the proposed method.Item Adaptive Backstepping Interval Type-2 Fuzzy Neural Network Control for Nonlinear Systems(2009-06-26) J.-H. Pan; W.-Y. Wang; Y.-G. Leu
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