Adaptive Neural Control for Output Feedback Nonlinear Systems Using a Barrier Lyapunov Function

Adaptive Neural Control for Output Feedback Nonlinear Systems Using a Barrier Lyapunov Function
Title:
Adaptive Neural Control for Output Feedback Nonlinear Systems Using a Barrier Lyapunov Function
Other Titles:
IEEE Transactions on Neural Networks
Keywords:
Publication Date:
01 July 2010
Citation:
Beibei Ren, Shuzhi Sam Ge, Keng Peng Tee, Tong Heng Lee. (2010). Adaptive Neural Control for Output Feedback Nonlinear Systems Using a Barrier Lyapunov Function. IEEE Transactions on Neural Networks, 21(8), 1339–1345. doi:10.1109/tnn.2010.2047115
Abstract:
In this brief, adaptive neural control is presented for a class of output feedback nonlinear systems in the presence of unknown functions. The unknown functions are handled via on-line neural network (NN) control using only output measurements. A barrier Lyapunov function (BLF) is introduced to address two open and challenging problems in the neuro-control area: 1) for any initial compact set, how to determine a priori the compact superset, on which NN approximation is valid; and 2) how to ensure that the arguments of the unknown functions remain within the specified compact superset. By ensuring boundedness of the BLF, we actively constrain the argument of the unknown functions to remain within a compact superset such that the NN approximation conditions hold. The semiglobal boundedness of all closed-loop signals is ensured, and the tracking error converges to a neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed approach.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1045-9227
1941-0093
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