Neural-network adaptive controller for nonlinear systems and its application in pneumatic servo systems
Lu LU
Fagui LIU
Weixiang SHI
摘要：In this paper, a novel control law is presented, which uses neural-network techniques to approximate the affine class nonlinear system having unknown or uncertain dynamics and noise disturbances. It adopts an adaptive control law to adjust the network parameters online and adds another control component according to H-infinity control theory to attenuate the disturbance. This control law is applied to the position tracking control of pneumatic servo systems. Simulation and experimental results show that the tracking precision and convergence speed is obviously superior to the results by using the basic BP-network controller and self-tuning adaptive controller.
机标关键词：control lawuncertain dynamicsnoise disturbancesnetwork parameterstracking controlnonlinear systemcontrol theory
分类号：TP13(自动化基础理论)TP18(自动化基础理论)
资助基金：Guangdong-Hong Kong Technology Cooperation Funding Scheme(2005A10207005;IID 2004-0005)香港研究资助局资助项目(9040407)
论文发表日期：2008-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：7( 97-103 )
英文信息