Please use this identifier to cite or link to this item: http://rportal.lib.ntnu.edu.tw:80/handle/77345300/36902
Title: Design of Adaptive Neural Net controller
Authors: 國立臺灣師範大學機電工程學系
Yeh, Zong-Mu
Issue Date: 22-May-1995
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Abstract: This paper presents an adaptive neural net controller for controlling given plants which are unknown. In the neural net structure, a two-layered network is used to emulate the unknown plant dynamics, and another two-layer neural network, which is the inverse of the estimator, is used to generate the control action on-line. A modified Widrow-Hoff delta rule is adopted as a learning algorithm to minimize the error between the real plant response and the output of the estimator. An effective learning method which is based on sliding motions is provided to tune the control action to improve the system performance and convergence. The major advantage of the proposed approach is that the lengthy training of the controller might be eliminated. The effectiveness of the proposed approach is illustrated through simulations of controlling a unstable plant and normalized motor model with noise disturbances.
URI: http://rportal.lib.ntnu.edu.tw/handle/77345300/36902
ISBN: 0-7803-2645-8
Other Identifiers: ntnulib_tp_E0402_02_010
Appears in Collections:教師著作

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