Announcement for Downloading full text filePlease respect the Copyright Act.
All digital full text dissertation and theses from this website are authorized the copyright owners. These copyrighted full-text dissertation and theses can be only used for academic, research and non-commercial purposes. Users of this website can search, read, and print for personal usage. In respect of the Copyright Act of the Republic of China, please do not reproduce, distribute, change, or edit the content of these dissertations and theses without any permission. Please do not create any work based upon a pre-existing work by reproduction, Adaptation, Distribution or other means.
URN etd-0609105-162207 Statistics This thesis had been viewed 2866 times. Download 1414 times. Author Kuo-Ho Su Author's Email Address firstname.lastname@example.org 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 Date of Defense 2005-05-05 Date of Submission 2005-06-09