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 應用於室內自然環境且可與人自然溝通之居家服務機器人--子計畫三：居家服務機器人之即時動態導航與定位學習之設計與實現(行政院國家科學委員會, 2010-07-31) 許陳鑑; 盧明智本計畫之主要目的在於研究居家服務機器人即時動態導航與定位之設計與實現，包 含：環境感知、導航(robot navigation)、以及迴避障礙等功能，使居家服務機器人能具有 自主認知與熟習環境的能力，以協助各個子計畫達成希望執行的任務。配合總計畫第 一、二年預定完成Robotcup@home 基礎及進階指定競賽項目的目標，本計畫將以：(1) 多感測器之障礙物偵測系統、(2)感測器資料融合、(3)虛擬環境地圖建立與定位、(4)自 主避障、(5)動態路徑規劃、以及(6)障礙空間資訊描述等六項研究重點，完成：(1)建立 一多重感知障礙物偵測系統，整合各種異質感測器之量測資訊，提供可靠之環境障礙物 資訊，(2)利用多種數據融合(sensor fusion)技術實現感測器資料融合，以提供可靠的量測 結果，作為建立地圖與避障之依據，(3)利用多種建地圖(map building)技術，建立並更新 未知環境之虛擬空間地圖，作為機器人自主導航的依據，(4)以多種啟引式演算法 (heuristic)以及演化計算法(evolutionary computation)實現最佳路徑之規劃，使機器人能據 以行動，快速且安全到達目的地，(5)依多重感測數據融合資料以及目標物移動方向之估 測，實現自主避障之功能，使機器人能安全行進，(6)利用多重感測數據融合資料作訊號 處理與座標轉換，實現障礙空間圖形化描述(obstacle profiling)，精確呈現障礙物空間資 訊。並與其他子計畫整合，使居家機器人能夠實現Robotcup@home 所指定之基礎及進 階競賽項目。第三年則針對適合台灣居家環境之機器人的相關項目進行研發，包含：(1) 提供居家空間多重輔助定位，包含超音波、RFID、以及影像式輔助定位，以校準機器人 位置，提供機器人精確定位，(2)建置即時遠端監控與整合式互動介面，方便使用者透過 遠端連線的方式進行監控，增加居家服務機器人的實用與便利性，(3)以嵌入式統軟硬體 協同設計(Hardware/Software Co-design)觀念，實現各種所提出之演算法，改善機器人定 位、路徑規劃、避障之執行效能，全面提升機器人之導航及避障性能。Item 應用於室內自然環境且可與人自然溝通之居家服務機器人--子計畫三：居家服務機器人之即時動態導航與定位學習之設計與實現(II)(行政院國家科學委員會, 2012-07-31) 許陳鑑; 簡忠漢本計畫之主要目的在於研究居家服務機器人即時動態導航與定位之設計與實現，包 含：環境感知、導航(robot navigation)、以及迴避障礙等功能，使居家服務機器人能具有 自主認知與熟習環境的能力，以協助各個子計畫達成希望執行的任務。配合總計畫第 一、二年預定完成RoboCup@home 基礎及進階指定競賽項目的目標，本計畫將以：(1) 多感測器之障礙物偵測系統、(2)感測器資料融合、(3)虛擬環境地圖建立與定位、(4)自 主避障、以及(5)動態路徑規劃等項目為研究重點，完成：(1)建立一多重感知障礙物偵 測系統，整合各種異質感測器之量測資訊，提供可靠之環境障礙物資訊，(2)利用多種數 據融合(sensor fusion)技術實現感測器資料融合，以提供可靠的量測結果，作為建圖、定 位與避障之依據，(3)利用多種地圖建置(map building)與自我定位技術，建立並更新未知 環境之虛擬地圖以及準確估測機器人的姿態，作為機器人自主導航的依據，(4)以多種啟 引式演算法(heuristic)以及演化計算法(evolutionary computation)實現最佳路徑之規劃，使 機器人能據以行動，快速且安全到達目的地，(5)依據多重感知障礙物偵測的結果以及目 標物移動方向之估測，實現自主避障之功能，使機器人能安全行進。並與其他子計畫整 合，使居家機器人能夠完成RoboCup@home 所指定之基礎及進階競賽項目。第三年的 目標主要是要延伸居家服務機器人的功能，針對適合台灣居家環境之機器人的相關項目 進行研發，包含：(1)利用影像式定位法配合目標物特徵辨識技術對障礙物進行偵測，以 確定目標物之空間座標位置，(2)提供居家空間多重輔助定位，包含超音波、RFID、以 及影像式輔助定位，以校準機器人位置，提供機器人精確定位，(3)利用多重感測數據融 合資料實現3D 地圖建立與圖形化介面設計，精確呈現障礙物空間資訊，(4)以嵌入式系 統軟硬體協同設計(Hardware/Software Co-design)觀念實現各種演算法，期使運算速度能 夠大幅提升，達到即時處理之需求，全面提升機器人之導航及避障性能。Item Discrete Modelling of Continuous-Time Systems Having Interval Uncertainties Using Genetic Algorithms(Institute of Electronics, Information and Communication Engineers, 2008-01-01) Chen-Chien Hsu; Tsung-Chi Lu; Heng-Chou ChenIn this paper, an evolutionary approach is proposed to obtain a discrete-time state-space interval model for uncertain continuous-time systems having interval uncertainties. Based on a worst-case analysis, the problem to derive the discrete interval model is first formulated as multiple mono-objective optimization problems for matrix-value functions associated with the discrete system matrices, and subsequently optimized via a proposed genetic algorithm (GA) to obtain the lower and upper bounds of the entries in the system matrices. To show the effectiveness of the proposed approach, roots clustering of the characteristic equation of the obtained discrete interval model is illustrated for comparison with those obtained via existing methods.Item Multiobjective Evolutionary Approach to the Design of Optimal Controllers for Interval Plants Based on Parallel Computation(Institute of Electronics, Information and Communication Engineers, 2006-09-01) Chen-Chien Hsu; Shih-Chi Chang; Chih-Yung YuDesign of optimal controllers satisfying performance criteria of minimum tracking error and disturbance level for an interval system using a multi-objective evolutionary approach is proposed in this paper. Based on a worst-case design philosophy, the design problem is formulated as a minimax optimization problem, subsequently solved by a proposed two-phase multi-objective genetic algorithm (MOGA). By using two sets of interactive genetic algorithms where the first one determines the maximum (worst-case) cost function values for a given set of controller parameters while the other one minimizes the maximum cost function values passed from the first genetic algorithm, the proposed approach evolutionarily derives the optimal controllers for the interval system. To suitably assess chromosomes for their fitness in a population, root locations of the 32 generalized Kharitonov polynomials will be used to establish a constraints handling mechanism, based on which a fitness function can be constructed for effective evaluation of the chromosomes. Because of the time-consuming process that genetic algorithms generally exhibit, particularly the problem nature of minimax optimization, a parallel computation scheme for the evolutionary approach in the MATLAB-based working environment is also proposed to accelerate the design process.�Item 線上基因演算之模糊類神經網路及其在非線性系統辨識與控制之應用(1/2)(行政院國家科學委員會, 2003-07-31) 王偉彥本計畫提出一種以基因演算為基礎輸出回授直接適應性模糊類神經控制器的設計法則，此控制器用以控制具未確定項之非線性動態系統。吾人使用一種reduced-form genetic algorithm (RGA)去調整模糊類神經控制器的權重因子，使得直接適應性模糊類神經控制器的權重因子可以基因演算方式線上調整。線上調整的適應函數是使用Lyapunov 設計方法推導。最後，加入監督式控制器確保控制系統的穩定性。Item A dynamic hierarchical fuzzy neural network for a general continuous function(2008-06-06) W.-Y. Wang; I-H. Li; S.-C. Li; M.-S. Tsai; S.-F. SuA serious problem limiting the applicability of the fuzzy neural networks is the "curse of dimensionality", especially for general continuous functions. A way to deal with this problem is to construct a dynamic hierarchical fuzzy neural network. In this paper, we propose a two-stage genetic algorithm to intelligently construct the dynamic hierarchical fuzzy neural network (HFNN) based on the merged-FNN for general continuous functions. First, we use a genetic algorithm which is popular for flowshop scheduling problems (GAFSP) to construct the HFNN. Then, a reduced-form genetic algorithm (RGA) optimizes the HFNN constructed by GAFSP. For a real-world application, the presented method is used to approximate the Taiwanese stock market.Item An online GA-based output-feedback direct adaptive fuzzy-neural controller for uncertain nonlinear systems(IEEE Systems, Man, and Cybernetics Society, 2004-02-01) W.-Y. Wang; C.-Y. Cheng; Y.-G. LeuIn this paper, we propose a novel design of a GA-based output-feedback direct adaptive fuzzy-neural controller (GODAF controller) for uncertain nonlinear dynamical systems. The weighting factors of the direct adaptive fuzzy-neural controller can successfully be tuned online via a GA approach. Because of the capability of genetic algorithms (GAs) in directed random search for global optimization, one is used to evolutionarily obtain the optimal weighting factors for the fuzzy-neural network. Specifically, we use a reduced-form genetic algorithm (RGA) to adjust the weightings of the fuzzy-neural network. In RGA, a sequential-search -based crossover point (SSCP) method determines a suitable crossover point before a single gene crossover actually takes place so that the speed of searching for an optimal weighting vector of the fuzzy-neural network can be improved. A new fitness function for online tuning the weighting vector of the fuzzy-neural controller is established by the Lyapunov design approach. A supervisory controller is incorporated into the GODAF controller to guarantee the stability of the closed-loop nonlinear system. Examples of nonlinear systems controlled by the GODAF controller are demonstrated to illustrate the effectiveness of the proposed method.Item A Dynamic Hierarchical Fuzzy Neural Network for A General Continuous Function(中華民國模糊學會, 2009-06-01) W.-Y. Wang; I-H. Li; S.-C. Li; M.-S. Tsai; S.-F. SuA serious problem limiting the applicability of the fuzzy neural networks is the “curse of dimensionality”, especially for general continuous functions. A way to deal with this problem is to construct a dynamic hierarchical fuzzy neural network. In this paper, we propose a two-stage genetic algorithm to intelligently construct the dynamic hierarchical fuzzy neural network (HFNN) based on the merged-FNN for general continuous functions. First, we use a genetic algorithm which is popular for flowshop scheduling problems (GA_FSP) to construct the HFNN. Then, a reduced-form genetic algorithm (RGA) optimizes the HFNN constructed by GA_FSP. For a real-world application, the presented method is used to approximate the Taiwanese stock market.