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 Fuzzy evaluation and expert system in classical control system design(1994-07-01) C.-H. Wang; W.-Y. Wang; T.-T. LeeThe purpose of this paper is to develop an expert system for control system design (ESCSD), with a unique set of fuzzy evaluation rules. The authors' investigation not only uses expert systems for control system design but also proposes a practical way to use a unique set of fuzzy evaluation rules to suggest a better design method for a given plant. A set of fuzzy evaluation rules extracted from four classical design procedures is proposed. It focuses on how to predict the results of design methods. The authors deem the fuzzy evaluation rules are predicting tools of an expert system. It is also shown in this paper that the set of fuzzy evaluation rules has been successfully integrated with ESCSD. Several examples are illustrated which show the agreeable result obtained from ESCSD.Item On constructing fuzzy membership functions and applications in fuzzy neural networks(1993-10-29) C.-H. Wang; T.-T. Lee; W.-Y. Wang; P.-S. TsengA unified form of fuzzy membership functions, called as B-spline membership functions (BMFs) is proposed. The computer simulation of fuzzy control of a model car is considered as an application of BMFs in fuzzy neural networks. The example shows that the number of iterations for learning is substantially less than that of conventional methods.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 Adaptive fuzzy-neural sliding mode control for a class of uncertain nonlinear dynamical systems(2001-03-24) W.-Y. Wang; M.-L. Chan; T.-T. LeeIn this paper, a novel design algorithm of adaptive fuzzy-neuralsliding mode control for a class of uncertain nonlinear dynamicalsystems is proposed to attenuate the effects caused by unmodeleddynamics, disturbances and approximate errors. Since fuzzy-neuralsystems can uniformly approximate nonlinear continuous functions toarbitrary accuracy, the adaptive fuzzy control theory is employed toderive the control law for a class of nonlinear system, with unknownnonlinear functions and disturbances. Moreover, the sliding modecontrol method is incorporated into the control law so that thederived controller is robust with respect to unmodeled dynamics,disturbances and approximate errors. To demonstrate the effectivenessof the proposed method, an example is illustrated in this paper.Item Evolutionary design of PID controller for twin rotor multi-input multi-output system(2002-06-14) W.-Y. Wang; T.-T. Lee; H.-C. HuangIn this paper, a framework to automatically generate a set of parameters of PID (proportional, integral and derivative) controllers for the twin rotor multi-input multi-output system (TRMS) by using a simplified genetic algorithm (GA) is proposed. The simplified GA is proposed for tuning the PID parameters. The sequential search method is used to find the desired crossover point for the crossover operation. Finally, the optimal PID parameters are applied to the TRMS. Simulation results and experimental verification are demonstrated to show the effectiveness and performance of the proposed method.Item GA-based learning of BMF fuzzy-neural network(2002-05-17) W.-Y. Wang; T.-T. Lee; C.-C. Hsu; Y.-H. LiAn approach to adjust both control points of B-spline membership functions (BMFs) and weightings of fuzzy-neural networks using a simplified genetic algorithm (SGA) is proposed. The SGA is proposed by using a sequential-search-based crossover point (SSCP) method in which a better crossover point is determined and only the gene at the specified crossover point is crossed as a single point crossover operation. Chromosomes consisting of both the control points of BMFs and the weightings of fuzzy-neural networks are coded as an adjustable vector with real number components and searched by the SGA. Because of the use of the SGA, faster convergence of the evolution process to search for an optimal fuzzy-neural network can be achieved. Nonlinear functions approximated by using the fuzzy-neural networks via the SGA are demonstrated to illustrate the applicability of the proposed methodItem 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 Robust sliding mode-like fuzzy logic control for anti-lock braking systems with uncertainties and disturbances(2003-11-05) W.-Y. Wang; K.-C. Hsu; T.-T. Lee; G.-M. ChenIn this paper, we propose a robust sliding mode-like fuzzy logic controller for an anti-lock brake system (ABS) with self-tuning of the dead-zone parameters. The main control strategy is to force the wheel slip ratio tracking the optimum value 0.2. The proposed controller for anti-lock braking systems provides a stable and reliable performance under the uncertainties in vehicle brake systems. Simulation results will show the validity and effectiveness of the proposed sliding mode-like fuzzy logic controller.Item On-line genetic fuzzy-neural sliding mode controller design(2005-10-12) P.-Z. Lin; W.-Y. Wang; T.-T. Lee; G.-M. ChenIn this paper, a novel online B-spline membership function (BMF) fuzzy-neural sliding mode controller trained by an adaptive bound reduced-form genetic algorithm (ABRGA) is developed to guarantee robust stability and tracking performance for robot manipulators with uncertainties and external disturbances. The general sliding manifold is used to construct the sliding surface and reduce the chattering of the control signal, which can be nonlinear or time varying. For the purpose of identification, the proposed BMF fuzzy-neural network trained by the ABRGA approximates the regressor of the manipulator. An adaptive bound algorithm is used to aid and speed up the searching speed of the RGA. Simulation results show that the proposed on-line ABRGA-based BMF fuzzy-neural sliding mode controller is effective and yields superior tracking performance for robot manipulators.Item MIMO Robust Control via Adaptive T-S Merged Fuzzy Models for Nonaffine Nonlinear Systems(2007-01-01) W.-Y. Wang; Y.-H. Chien; I-H. Li; T.-T. Lee