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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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
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