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 設計與實現差動型輪型移動機器人之機器人控制系統(2023) 鍾秉剛; Jhong, Bing-Gang本論文改良機器人控制系統中的演算法，主題涵蓋機器人的運動規劃、定位與控制器設計，藉此提升控制系統的運作效率。在運動規劃領域，我們探討或提出對雙向快速探索隨機樹（BRRT）演算法、A*演算法與hybrid A*演算法的改進措施，並且設計剪枝與平滑算法優化路徑品質，最後搭配梯形速度規劃完成運動規劃工作。在定位方面，在使用特徵地圖的場合採用拓展卡曼濾波器，而在網狀地圖使用改良式蒙地卡羅定位法。此改良式蒙地卡羅定位法由本論文提出，藉由重新設計演算法的權重分配與重新採樣的架構提升演算法的搜尋效率。而在控制器設計方面，我們提出了一種自適應控制器，旨在最小化機器人的預定狀態和當前狀態之間的追蹤誤差。透過我們的機器人控制系統，機器人可以順利地從目前位置導航到指定目標。該系統的性能透過模擬和實驗結果的結合得到證實。Item 多重智慧控制器應用於機械手臂定位(2013) 許哲勝本論文的主要目的是設計一個六軸機械手臂，並且實現高精密且高穩定之六軸機械手臂。並且在硬體架構、機械手臂之空間三維座標、機械手臂各關節轉動角度與定位追跡的控制作介紹。在空間座標轉換中，本研究使用了D-H 座標系統來運算，並且求得機械手臂中各軸關節之轉換矩陣，再藉由順向運動學與逆向運動學的理論求得機械手臂之空間三維座標與機械手臂各關節轉動角度之轉換關係，並且再藉由設計控制器完成定位控制與追跡控制。 在控制器設計方面，本論文也設計一個多重人工智慧控制器去控制此六軸機械手臂。在控制的過程中，系統會有外界的干擾與不穩定因素，因此本研究所使用之適應性模糊類神經網路控制器會藉由理想輸出位置與機械手臂實際位置之誤差的回授來調整控制器的內部參數，藉由控制器自行調整其內部參數，則可達到高精密與高穩定度的控制法則。最後也提出李阿普諾函式(Lyapunov function)來證明此控制機械手臂系統之穩定性。Item 以小波基底函數為基礎的適應性倒階控制應用於具有驅動飽和限制之不確定系統(2010) 高暉翔; Hui-Hsiang Kao本篇論文提出三種非線性系統的控制方法。首先，在第二章先提出一個小波基底函數適應性倒階典型非線性系統的控制器。本文提出小波類神經自適應倒階控制器來控制未知的非線性系統。這個控制器結合小波基底函數與驅動飽和限制。接著，在第三章提出一個小波基底函數適應性倒階非典型非線性系統的控制器。本文中推薦在非典型非線性系統使用小波適應性倒階去控制。此控制方法結合適應性倒階控制器及小波基底函數，並以此方法近似未知的動態系統，小波基底函數擁有良好的近似性能，它較適合線上動態系統藉由調整內部的參數，以均值定理設計參數適應律來避免基底微分，且使用濾波器在控制輸入上減少基底微分計算量。最後，本篇論文提出結合小波適應性倒階與一階濾波器的設計概念來控制非典型非線性系統。而系統的穩定性以李亞普諾夫函數方程式分析說明，再以電腦舉例模擬論證本文所提出方法之控制性能與應用性。Item A performance approach to fuzzy control design for nonlinear systems(Elsevier, 1994-06-24) Yeh, Zong-MuThis paper presents a systematic methodology to the design of a decentralized fuzzy logic controller for large-scale nonlinear systems. A new method which is based on a performance index of sliding mode control is used to derive fuzzy rules and an adaptive algorithm is used to reduce the chattering phenomenon. The simulation results of a two-inverted pendulum system and a two-link manipulator demonstrate that the attractive features of the proposed approach include a smaller residual error and robustness against nonlinear interactions.Item Compact Ant Colony Optimization Algorithm Based Fuzzy Neural Network Backstepping Controller for MIMO Nonlinear Systems(2010-07-03) W.-Y. Wang; C.-K. Chen; Y.-G. Leu; C.-Y. ChenIn this paper, a compact ant colony algorithm used to tune parameters of fuzzy-neural networks is proposed for function approximation and adaptive control of nonlinear systems. In adaptive control procedure for nonlinear systems, weights of the fuzzy neural controller are online adjusted by the compact ant algorithm in order to generate appropriate control input. For the purpose of evaluating the stability of the closed-loop systems, an energy fitness function is used in the ant algorithm. Finally, a computer simulation example demonstrates the feasibility and effectiveness of the proposed method.Item Adaptive Nonlinear Parametric Neural Control of Nonaffine Nonlinear Systems(2010-07-03) Y.-G. Leu; W.-C. Leu; W.-Y. Wang; Z.-H. LeeBy using B-spline neural networks, an adaptive nonlinear parametric control scheme for nonlinear systems is proposed in this paper. The control scheme which is utilized to design the control input incorporates the adaptive control design technique with the mean-estimation B-spline neural networks. Compared with other neural networks, the B-spline neural networks possess output behavior the characteristic feature of locally controlling. Therefore, they are very suitable to online estimate system dynamics by tuning both control and knot points. The B-spline neural networks with a mean苟stimation technique are used in an attempt to avoid difficulty of differentiating B-spline basis functions. In addition, two robust controllers are used to compensate un modeling dynamics. Finally, an example is provided to demonstrate the feasibility of the proposed scheme, and a comparative study is given by computer simulation.Item Observer-based adaptive fuzzy-neural control for unknown nonlinear dynamical systems(IEEE Systems, Man, and Cybernetics Society, 1999-10-01) Y.-G. Leu; T.-T. Lee; W.-Y. WangIn this paper, an observer-based adaptive fuzzy-neural controller for a class of unknown nonlinear dynamical systems is developed. The observer-based output feedback control law and update law to tune on-line the weighting factors of the adaptive fuzzy-neural controller are derived. The total states of the nonlinear system are not assumed to be available for measurement. Also, the unknown nonlinearities of the nonlinear dynamical systems are not restricted to the system output only. The overall adaptive scheme guarantees that all signals involved are bounded. Simulation results demonstrate the applicability of the proposed method in order to achieve desired performanceItem Observer-Based Direct Adaptive Fuzzy-Neural Control for Anti-lock Braking Systems(中華民國模糊學會, 2006-12-01) G.-M. Chen; W.-Y. Wang; T.-T. Lee; C.-W. TaoIn this paper, an observer-based direct adaptive fuzzy-neural controller (ODAFNC) for an anti-lock braking system (ABS) is developed under the constraint that only the system output, i.e., the wheel slip ratio, is measurable. The main control strategy is to force the wheel slip ratio to well track the optimal value, which may vary with the environment. The observer-based output feedback control law and update law for on-line tuning of the weighting factors of the direct adaptive fuzzy-neural controller are derived. By using the strictly-positive-real (SPR) Lyapunov theory, the stability of the closed-loop system can be guaranteed. Simulation results demonstrate the effectiveness of the proposed control scheme forABS control.