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 基於區間第二類模糊類神經網路之螞蟻群聚最佳化演算法與其在直流馬達之應用(2010) 陳弨廣; Chao-Kuang Chen本文提出一個使用螞蟻群聚最佳化演算法來調整區間第二類模糊類神經網路的參數，並將其應用於函數近似與非線性系統之適應控制器設計。區間第二類模糊系統涵蓋了第一類模糊系統，使得我們可以掌握更多系統的不確定性。在非線性系統之適應控制過程中，區間第二類模糊類神經控制器的權重値是經由螞蟻群聚最佳化演算法來即時調整，以產生適當的控制輸入。為了即時評估閉迴路系統穩定的趨勢，本文使用李亞普諾夫函數來分析其穩定性。並提出一個能量適應函數於螞蟻群聚最佳化演算法中，藉此獲得較佳的閉迴路系統的穩定度。此外，由於螞蟻群聚最佳化演算法可能在線上即時控制過程中使系統狀態進入不穩定的區域。因此，在控制結構中加入了監督控制，限制系統的狀態在穩定的範圍內。本文藉由電腦模擬結果驗證所提出方法的可行性與效能。最後，將此控制法則應用在直流伺服馬達追蹤控制實驗。Item 遺傳演算模糊類神經與其在直流伺服馬達上之應用(2009) 林建佑; Jian-You Lin本文提出一個使用小型的遺傳演算法來調整模糊類神經網路的參數，並將其應用於函數近似與非線性系統之適應控制器設計。此小型的遺傳演算法應用於適應控制器設計，不需要事先離線學習的程序和複雜的數學運算。相較於傳統非線性系統的適應控制器，可有效減少適應控制器所需複雜的數學運算。在非線性系統之適應控制過程中，模糊類神經控制器的權重値是經由遺傳演算法來即時調整，以產生適當的控制輸入。為了即時評估閉迴路系統穩定的趨勢，本文從里亞布諾夫（Lyapunov）函數的穩定性分析推導過程中，提出一個能量適應函數於小型的遺傳最佳演算法中，藉此獲得較佳的閉迴路系統的穩定度。此外，由於小型的遺傳演算法可能在即時控制過程中使系統狀態進入不安全的區域。因此，加入安全控制器以限制閉迴路系統的狀態進入不安全的區域。 本文藉由電腦模擬結果驗證所提出方法的可行性與效能。最後，將此模糊類神經控制器應用在直流伺服馬達追蹤控制實驗。Item 簡化退火演算法基於模糊類神經網路控制器於非線性系統之控制(2009) 廖建豪; Jian-Hao Liao本文提出一個利用簡化的模擬退火演算法來調整模糊類神經網路的參數，並將其應用於函數近似與非線性系統之適應控制器設計。此簡化的模擬退火演算法應用於適應控制器設計，不需要事先離線學習的程序和複雜的數學運算。相較於傳統非線性系統的適應控制器，可有效減少適應控制器所需複雜的數學運算。在非線性系統之適應控制過程中，模糊類神經控制器的權重値是經由模擬退火演算法來即時調整，以產生適當的控制輸入。為了即時評估閉迴路系統穩定的趨勢，本文從Lyapunov函數的推導過程中，提出一個能量成本函數於簡化的模擬退火最佳演算法中，藉著獲得較佳的閉迴路系統的穩定度。此外，由於簡化模擬退火法，可能在即時控制過程中使系統狀態進入不安全的區域。因此，加入監督控制器以限制閉迴路系統的狀態進入不安全的區域。 本文藉由電腦模擬結果驗證所提出方法的可行性與效能。最後，將此模糊類神經控制器應用在直流伺服馬達追蹤控制實驗。Item Adaptive Nonlinear Parametric Neural Control of Nonaffine Nonlinear Systems(2010-07-03) Y.-G. Leu; W.-C. Leu; W.-Y. Wang; Z.-H. LeeBy using B-spline neural networks, an adaptive nonlinear parametric control scheme for nonlinear systems is proposed in this paper. The control scheme which is utilized to design the control input incorporates the adaptive control design technique with the mean-estimation B-spline neural networks. Compared with other neural networks, the B-spline neural networks possess output behavior the characteristic feature of locally controlling. Therefore, they are very suitable to online estimate system dynamics by tuning both control and knot points. The B-spline neural networks with a mean苟stimation technique are used in an attempt to avoid difficulty of differentiating B-spline basis functions. In addition, two robust controllers are used to compensate un modeling dynamics. Finally, an example is provided to demonstrate the feasibility of the proposed scheme, and a comparative study is given by computer simulation.Item 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. WangIn 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 performanceItem Robust adaptive fuzzy-neural control of nonlinear dynamical systems using generalized projection update law and variable structure controller(IEEE Systems, Man, and Cybernetics Society, 2001-02-01) W.-Y. Wang; Y.-G. Leu; C.-C. HsuIn this paper, a 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 generalized projection update law, which generalizes 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 to the specified regions. Moreover, a 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. To demonstrate the effectiveness of the proposed method, several examples are illustrated in this paperItem Observer-Based Direct Adaptive Fuzzy-Neural Control for Anti-lock Braking Systems(中華民國模糊學會, 2006-12-01) G.-M. Chen; W.-Y. Wang; T.-T. Lee; C.-W. TaoIn this paper, an observer-based direct adaptive fuzzy-neural controller (ODAFNC) for an anti-lock braking system (ABS) is developed under the constraint that only the system output, i.e., the wheel slip ratio, is measurable. The main control strategy is to force the wheel slip ratio to well track the optimal value, which may vary with the environment. The observer-based output feedback control law and update law for on-line tuning of the weighting factors of the direct adaptive fuzzy-neural controller are derived. By using the strictly-positive-real (SPR) Lyapunov theory, the stability of the closed-loop system can be guaranteed. Simulation results demonstrate the effectiveness of the proposed control scheme forABS control.Item Robust H-inf. output feedback control for discrete-time nonaffine nonlinear systems with structured uncertainties(IMF, 2006-01-01) J.-L. Wu; W.-Y. Wang; T.-T. LeeIn this paper, the robust output feedback H ∞ control problem for discrete time general nonlinear systems with L2-bounded structured uncertainties is considered. Sufficient conditions for the solvability of the considered problem are represented in terms of two Hamilton-Jacobi inequalities with n independent variables. Based on these conditions, a state space characterization of a robust output feedback H ∞ controller solving the considered problem is provided.Item Image-based fuzzy control system(Institution of Engineering and Technology, 2008-03-27) G.-M. Chen; P.-Z. Lin; W.-Y. Wang; T.-T. Lee; C.-H WangA novel image-based fuzzy control (IBFC) scheme is developed to imitate the way humans use visual information to control objects. A CCD camera gathers images of the controlled plant, and a simple algorithm analyses the images. The proposed image analysis algorithm utilises image information more intuitively than visual servo control systems. The difference between a reference image and the current image is numerically expressed and directly used by a fuzzy control system using a human-like control law. To investigate the effectiveness of the proposed IBFC scheme, it is applied to control an inverted pendulum system. Simulation results show that the IBFC system can achieve favourable tracking performance without prior knowledge of the controlled plant.