| Dissipative-based adaptive neural control for nonlinear systems |
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| 引用本文： | Yugang Niu,Xingyu Wang,Junwei Lu. Dissipative-based adaptive neural control for nonlinear systems[J]. 控制理论与应用(英文版), 2004, 2(2): 126-130. DOI: 10.1007/s11768-004-0056-0 |
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| 作者姓名： | Yugang Niu Xingyu Wang Junwei Lu |
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| 作者单位： | [1]SchoolofInformationScience＆Engineering,EastChinaUniversityofScience＆Technology,Shanghai200237,China [2]CollegeofElectricalandElectronicEngineering,NanjingNormalUniversity,NanjingJiangsu210042,China |
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| 摘 要： | A dissipative-based adaptive neural control scheme was developed for a class of nonlinear uncertain systems with unknown nonlinearities that might not be linearly parameterized. The major advantage of the present work was to relax the requirement of matching condition, i.e., the unknown nonlinearities appear on the same equation as the control input in a state-space representation, which was required in most of the available neural network controllers. By synthesizing a state-feedback neural controller to make the closed-loop system dissipative with respect to a quadratic supply rate, the developed control scheme guarantees that the L2-gain of controlled system was less than or equal to a prescribed level. And then, it is shown that the output tracking error is uniformly ultimate bounded. The design scheme is illustrated using a numerical simulation.
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| 关 键 词： | 非线性系统 自适应控制 神经网络 损耗理论 |
| 收稿时间： | 2002-09-09 |
| 修稿时间： | 2003-06-16 |
Dissipative-based adaptive neural control for nonlinear systems |
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| Yugang Niu,Xingyu Wang,Junwei Lu. Dissipative-based adaptive neural control for nonlinear systems[J]. Journal of Control Theory and Applications, 2004, 2(2): 126-130. DOI: 10.1007/s11768-004-0056-0 |
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| Authors: | Yugang Niu Xingyu Wang Junwei Lu |
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| Affiliation: | 1. School of Information Science & Engineering, East China University of Science & Technology, Shanghai 200237, China
2. College of Electrical and Electronic Engineering, Nanjing Normal University, Nanjing Jiangsu 210042, China |
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| Abstract: | A dissipative-based adaptive neural control scheme was developed for a class of nonlinear uncertain systems with unknown nonlinearities that might not be linearly parameterized. The major advantage of the present work was to relax the requirement of matching condition, i.e., the unknown nonlinearities appear on the same equation as the control input in a state-space representation, which was required in most of the available neural network controllers. By synthesizing a state-feedback neural controller to make the closed-loop system dissipative with respect to a quadratic supply rate, the developed control scheme guarantees that the L2-gain of controlled system was less than or equal to a prescribed level. And then, it is shown that the output tracking error is uniformly ultimate bounded. The design scheme is illustrated using a numerical simulation. |
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| Keywords: | Nonlinear systems Adaptive control Dissipative theory Neural networks |
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