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 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. LeeA 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 approachItem Robust Adaptive Controller Design for a Class of Uncertain Nonlinear Systems Using Online T–S Fuzzy-Neural Modeling Approach(IEEE Systems, Man, and Cybernetics Society, 2011-04-01) Y.-H. Chien; W.-Y. Wang; Y.-G. Leu; T.-T. LeeThis paper proposes a novel method of online modeling and control via the Takagi–Sugeno (T–S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T–S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T–S fuzzy control methods can model and control them only with great difficulty, if at all. Instead of modeling these uncertain functions directly, we propose that a T–S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identification algorithm for the VLS and put significant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paperItem Hybrid Intelligent Output-Feedback Control for Trajectory Tracking of Uncertain Nonlinear Multivariable Dynamical Systems(中華民國模糊學會, 2012-03-01) Y.-H. Chien; W.-Y. Wng; I-H. Li; K.-Y. Lian; T.-T. LeeOutput-feedback control for trajectory tracking is an important research topic of various engineering systems. In this paper, a novel online hybrid direct/indirect adaptive Petri fuzzy neural network (PFNN) controller with stare observer for uncertain nonlinear multivariable dynamical systems using generalized projection-update laws is presented. This new approach consists of control objectives determination, approximator configuration design, system dynamics modeling, online control algorithm development, and system stability analysis. According to the importance and viability of plant knowledge and control knowledge, a weighting factor is utilized to sum together the direct and indirect adaptive PFNN controllers. Therefore, the controller design methodology is more flexible during the design process. Besides, an improved generalized projection-update law is utilized to tune the adjustable parameters to prevent parameter drift. To illustrate the effectiveness of the proposed online hybrid PFNN controller and observer-design methodology, numerical simulation results for inverted pendulum systems and rigid robot manipulators are given in this paper.