INTELLIGENT ADAPTIVE CONTROL SYSTEM DESIGN FOR NONLINEAR SYSTEMS USING AUTO-LEARNING ALGORITHM

博士 === 元智大學 === 電機工程學系 === 100 === This dissertation focused on the design of intelligent control systems based on the adaptive control and auto-learning algorithm for uncertain nonlinear systems. The proposed intelligent control systems include a neural network (NN) control system, and a cerebellar...

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Bibliographic Details
Main Authors: Ang-Bung Ting, 丁安邦
Other Authors: 林志民
Format: Others
Language:en_US
Online Access:http://ndltd.ncl.edu.tw/handle/01980001345542204127
Description
Summary:博士 === 元智大學 === 電機工程學系 === 100 === This dissertation focused on the design of intelligent control systems based on the adaptive control and auto-learning algorithm for uncertain nonlinear systems. The proposed intelligent control systems include a neural network (NN) control system, and a cerebellar model articulation controller (CMAC). The developed control scheme is comprised of a main controller and an auxiliary compensation controller. The main controller, including an NN controller or a CMAC, is utilized to approximate an ideal controller, and an auxiliary compensation controller is utilized to attenuate the residual of approximation error with guaranteed stability or specified tracking performance. The auto-learning algorithms can adjust the parameters of intelligent control system by using the tracking error and without the need for preliminary knowledge. The on-line parameter auto-learning methodologies using both of the gradient descent method and the Lyapunov stability theorem are developed to increase the system learning capability and to guarantee the stability of the system. The developed design methods are then applied to some control systems, such as two-link manipulator systems, unified chaotic circuit, a mass-spring-damper mechanical systems, data fusion systems and brushless DC (BLDC) motors to demonstrate the effectiveness of the proposed design methods.