首頁 > 網路資源 > 大同大學數位論文系統

Title page for etd-0609105-162207


URN etd-0609105-162207 Statistics This thesis had been viewed 2866 times. Download 1414 times.
Author Kuo-Ho Su
Author's Email Address khsu@ntist.edu.tw
Department Electrical Engineering
Year 2004 Semester 2
Degree Ph.D. Type of Document Doctoral Dissertation
Language English Page Count 150
Title DESIGN AND APPLICATIONS OF HYBRID INTELLIGENT CONTROLLERS
Keyword
  • servo motor drive
  • fuzzy control
  • genetic algorithm
  • grey theorem
  • grey theorem
  • genetic algorithm
  • fuzzy control
  • servo motor drive
  • Abstract Some hybrid intelligent controllers are designed for nonlinear dynamical systems in this dissertation. First, a newly-design adaptive fuzzy total sliding-mode controller (AFTSMC), in which a translation width is embedded into the fuzzy controller to reduce the chattering phenomena, is developed for perturbed electrical servo drive and tension control. In the AFTSMC, the fuzzy control rules base is compact and only one parameter needs to be adjusted. The second controller is also developed for perturbed electrical servo drive but it is designed via the approximation ability of fuzzy system to mimic the good behaviors of total sliding-mode control (TSMC) system. The third control scheme is named as supervisory enhanced genetic algorithm controller (SEGAC). It includes an enhanced genetic algorithm controller (EGAC) and a supervisory controller. In the EGAC design, the spirit of gradient descent training is embedded in GA to construct a main controller to search the optimum control effort under uncertainties. Moreover, to stabilize the system states around a defined bound region, a supervisory controller, which is derived in the sense of Lyapunov stability theorem, is added to adjust the control effort. Finally, a supervisory state feedback linearization control via grey uncertainty prediction technique is proposed to track the desired trajectory under the environment that unmodelled dynamics and external disturbances exist. The grey uncertainty predictor is designed to forecast the uncertainty and the predicted data is fed to the feedback linearization controller to evaluate the control effort on line. All the proposed control schemes are applied to electrical servo drives or other nonlinear dynamical systems by simulation and experiment to demonstrate the effectiveness and advantages.
    Advisor Committee
  • Chung-Chun Kung - advisor
  • Chau-Yun Hsu - co-chair
  • Chen, Bor-Sen - co-chair
  • Chiang-Cheng Chiang - co-chair
  • Ching-Chang Wong - co-chair
  • Wen-June Wang - co-chair
  • Files indicate access worldwide
    Date of Defense 2005-05-05 Date of Submission 2005-06-09


    Browse | Search All Available ETDs