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| Received:September 09, 2002Revised:June 16, 2003 |
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| Dissipative-based adaptive neural control for nonlinear systems |
| Yugang Niu, Xingyu Wang,Junwei Lu |
| (School of Information Science & Engineering, East China University of Science & Technology, 200237 Shanghai, China;College of Electrical and Electronic Engineering, Nanjing Normal University, 210042 Nanjing, Jiangsu, China) |
| 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. |
| Key words: Nonlinear systems Adaptive control Dissipative theory Neural networks |
