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 A GA-based indirect adaptive fuzzy-neural controller for uncertain nonlinear systems(2002-12-06) W.-Y. Wang; C.-C. Hsu; C.-W. Tao; Y.-H. LiIn this paper, a novel approach to adjust both the control points of B-spline membership functions (BMFs) and the weightings of fuzzy-neural networks using a reduced-form genetic algorithm (RGA) is proposed. 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 RGA. Moreover, we propose an application of the RGA in designing an RGA-based indirect adaptive fuzzy-neural controller (RIAFC) for uncertain nonlinear dynamical systems. The free parameters of the indirect adaptive fuzzy-neural controller can successfully be tuned on-line via the RGA approach. A supervisory controller is incorporated into the RIAFC to stabilize the closed-loop nonlinear system. An example of a nonlinear system controlled by RIAFC are demonstrated to show the effectiveness of the proposed method.Item 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 A composite controller for unknown nonlinear dynamical systems using robust adaptive fuzzy-neural control schemes(2000-09-27) W.-Y. Wang; C.-C. Hsu; Y.-G. LeuA robust adaptive fuzzy-neural control scheme for nonlinear dynamical systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbance and modeling errors. A composite update law, which has a generalized form combining the projection algorithm modification and the switching-σ adaptive law, is used to tune the adjustable parameters for preventing parameter drift and confining states of the system into the specified regions. Moreover, a fuzzy variable structure control method is incorporated into the control law so that the derived controller is robust with respect to unmodeled dynamics, disturbances and modeling errors. Compared with previous control schemes for nonlinear systems, the magnitude of the control input by using the proposed approach is much smaller, which is a significant advantage in designing controllers for practical applications. To demonstrate the effectiveness and applicability of the proposed method, several examples are illustrated in the paperItem Model reduction of sampled systems using an enhanced multiresolutional dynamci genetic algorithm(2001-06-01) C.-C. H; W.-Y. Wang; C.-Y. YuItem Discrete-Time Model Reduction of Sampled Systems Using an Enhanced Multiresolutional Dynamic Genetic Algorithm(2001-10-10) C.-C. Hsu; K.-M. Tse; W.-Y. WangA framework to automatically generate a reduced-order discrete-time model for the sampled system of a continuous plant preceded by a zero-order hold (ZOH) using an enhanced multi-resolution dynamic genetic algorithm (EMDGA) is proposed in this paper. Chromosomes consisting of the denominator and the numerator parameters of the reduced-order model are coded as a vector with floating-point-type components and searched by the genetic algorithm. Therefore, a stable optimal reduced-order model satisfying the error range specified can be evolutionarily obtained. Because of the use of the multi-resolution dynamic adaptation algorithm and the genetic operators, the convergence rate of the evolution process to search for an optimal reduced-order model can be expedited. Another advantage of this approach is that the reduced discrete-time model evolves based on samples taken directly from the continuous plant, instead of the exact discrete-time model, so that computation time is savedItem Genetic algorithms-derived digital integrators and their applications in discretization of continuous systems(2002-01-01) C.-C. Hsu; W.-Y. Wang; C.-Y. YuA set of enhanced digital integrators (EDI) with improved accuracy via genetic algorithms are proposed in this paper. By specifying a desired power for the integrator to be sought and the interval for comparison, chromosomes consisting of parameters of the enhanced digital integrator are then searched by the genetic algorithm based on root mean squared (RMS) error between the original integrator and candidates of the enhanced digital integrator. Thus, all the best parameters of an optimal enhanced digital integrator can be evolutionarily obtained. To demonstrate the effectiveness of the proposed approach, the derived enhanced digital integrators are used to obtain the discrete approximation for continuous systems.Item Sliding Control for Linear Uncertain Systems(2003-09-19) C.-W. Tao; M.-L. Chan; W.-Y. WangA new design approach to enhance a terminal sliding mode controller for linear systems with mismatched time-varying uncertainties is presented in this paper. The nonlinear sliding surface is used to have the system states arrive at the equilibrium point in the finite time period. The sliding coefficient matching condition is extended for the terminal sliding mode control. The uncertain system with the proposed terminal sliding mode controller is shown to be invariant on the sliding surface. The reaching mode of the sliding surface is guaranteed and the close-loop system is stable. Moreover, the undesired chattering is alleviated with the designed terminal sliding mode controller. Simulation results are included to illustrate the effectiveness of the presented terminal sliding mode controller.Item Sliding mode control for uncertain nonlinear system with multiple inputs containing sector nonlinearities and deadzones(2003-01-01) W.-Y. Wang; K.-C. Hsu; P.-Z. LinItem Robust control of the mismatched systems with the fuzzy integral sliding controller(2003-10-08) C.-W. Tao; M.-L. Chan; W.-Y. WangAn adaptive fuzzy integral sliding mode controller for mismatched time-varying linear systems is presented in this paper. The proposed fuzzy integral sliding mode controller is designed to have zero steady state system error under step inputs and alleviate the undesired chattering around the sliding surface. The parameters in the fuzzy mechanism are adapted on-line to improve the performance of the fuzzy integral sliding mode control system. Thus, the bounds of the uncertainties are not required to be known in advance. The designed fuzzy integral sliding mode control system is shown to be invariant on the sliding surface. Moreover, the reaching mode of the sliding surface is guaranteed and the close-loop system is stable. Simulation results are included to illustrate the effectiveness of the presented fuzzy integral sliding mode controller.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.