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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系所網址：https://www.ee.ntnu.edu.tw/

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Author: search.filters.author.P.-Z. LinHas files: No

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Now showing 1 - 7 of 7
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    GA-based fuzzy-neural sliding mode controllers of robot manipulators
    (2004-01-01) P.-Z. Lin; W.-Y. Wang; T.-T. Lee; Y.-G. Leu
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    On-line genetic fuzzy-neural sliding mode controller design
    (2005-10-12) P.-Z. Lin; W.-Y. Wang; T.-T. Lee; G.-M. Chen
    In 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.
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    Fuzzy Control Using Intuitive Image Analysis
    (2008-05-27) G.-M. Chen; P.-Z. Lin; W.-Y. Wang; T.-T. Lee; C.-H. Wang
    In this paper, a novel fuzzy control scheme using intuitive image analysis is developed to imitate the intuitive human control behavior determined through human eyes. A CCD camera is used to gather the images of the controlled plant, and a simple algorithm is proposed to analyze the images. Unlike that in the visual servo control systems, the image information is utilized in a more intuitive way via the proposed image analysis algorithm. 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 fuzzy control scheme, it is applied to an inverted pendulum system. Simulation results show that the proposed scheme can achieve favorable tracking performance without prior knowledge of the controlled plant.
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    Sliding mode control for uncertain nonlinear systems with multiple inputs containing sector nonlinearities and deadzones
    (IEEE Systems, Man, and Cybernetics Society, 2004-02-01) K.-C. Hsu; W.-Y. Wang; P.-Z. Lin
    In this paper, we investigate a novel robust control approach for a class of uncertain nonlinear systems with multiple inputs containing sector nonlinearities and deadzones. Sliding mode control (SMC) is suggested to design stabilizing controllers for these uncertain nonlinear systems. The controllers guarantee the global reaching condition of the sliding mode in these systems. They can work effectively for systems either with or without sector nonlinearities and deadzones in the inputs. Moreover, the controllers ensure that the system trajectories globally exponentially converge to the sliding mode. Illustrative examples are demonstrated to verify the effectiveness of the proposed sliding mode controller.
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    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 Wang
    A 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.
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    On-Line Genetic Algorithm-Based Fuzzy-Neural Sliding Mode Controller Using Improved Adaptive Bound Reduced-Form Genetic Algorithm
    (Taylor & Francis, 2009-06-01) P.-Z. Lin; W.-Y. Wang; T.-T. Lee; C.-H. Wang
    In this article, a novel on-line genetic algorithm-based fuzzy-neural sliding mode controller trained by an improved adaptive bound reduced-form genetic algorithm is developed to guarantee robust stability and good tracking performance for a robot manipulator with uncertainties and external disturbances. A general sliding manifold, which can be non-linear or time varying, is used to construct a sliding surface and reduce control law chattering. In this article, the sliding surface is used to derive a genetic algorithm-based fuzzy-neural sliding mode controller. To identify structured system dynamics, a B-spline membership function fuzzy-neural network, which is trained by the improved genetic algorithm, is used to approximate the regressor of the robot manipulator. The sliding mode control with a general sliding surface plays the role of a compensator when the fuzzy-neural network does not approximate the dynamics regressor of the robot manipulator well in the transient period. The adjustable parameters of the fuzzy-neural network are tuned by the improved genetic algorithm, which, with the use of the sequential-search-based crossover point method and the single gene crossover, converges quickly to near-optimal parameter values. Simulation results show that the proposed genetic algorithm-based fuzzy-neural sliding mode controller is effective and yields superior tracking performance for robot manipulators.