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Adaptive L2 control of nonlinear systems using neural networks
引用本文:Huaijing QU,Ying ZHANG,Fengrong SUN. Adaptive L2 control of nonlinear systems using neural networks[J]. 控制理论与应用(英文版), 2004, 2(4): 332-338. DOI: 10.1007/s11768-004-0036-4
作者姓名:Huaijing QU  Ying ZHANG  Fengrong SUN
作者单位:[1]SchoolofInformationScienceandEngineering,ShandongUniversity,JinanShandong250100,China [2]SingaporeInstituteofManufacturingTechnology,NanyangDrive71638075,Singapore
摘    要:An adaptive neural network controller is developed to achieve output-tracking of a class of nonlinear systems. The global L2 stability of the closed-loop system is established. The proposed control design overcomes the limitation of the conventional adaptive neural control design where the modeling error brought by neural networks is assumed to be bounded over a compact set.Moreover,the generalized matching conditions are also relaxed in the proposed L2 control design as the gains for the external disturbances entering the system are allowed to have unknown upper bounds.

关 键 词:自适应控制 神经网络 非线性系统 稳定性
收稿时间:2003-12-26

Adaptive L2 control of nonlinear systems using neural networks
Huaijing QU,Ying ZHANG,Fengrong SUN. Adaptive L2 control of nonlinear systems using neural networks[J]. Journal of Control Theory and Applications, 2004, 2(4): 332-338. DOI: 10.1007/s11768-004-0036-4
Authors:Huaijing QU  Ying ZHANG  Fengrong SUN
Affiliation:1. School of Information Science and Engineering,Shandong University,Jinan Shandong 250100,China
2. Singapore Institute of Manufacturing Technology,Nanyang Drive 71 638075,Singapore
Abstract:An adaptive neural network controller is developed to achieve output-tracking of a class of nonlinear systems. The global L 2 stability of the closed-loop system is established. The proposed control design overcomes the limitation of the conventional adaptive neural control design where the modeling error brought by neural networks is assumed to be bounded over a compact set. Moreover, the generalized matching conditions are also relaxed in the proposed L 2 control design as the gains for the external disturbances entering the system are allowed to have unknown upper bounds.
Keywords:Adaptive control  neural network  Nonlinear systems  Stability  L2 controller   Backstepping design
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