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Globally Adaptive Neural Network Tracking for Uncertain Output-Feedback Systems
- Source :
- IEEE Transactions on Neural Networks and Learning Systems. 34:814-823
- Publication Year :
- 2023
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2023.
-
Abstract
- This article investigates the problem of global neural network (NN) tracking control for uncertain nonlinear systems in output feedback form under disturbances with unknown bounds. Compared with the existing NN control method, the differences of the proposed scheme are as follows. The designed actual controller consists of an NN controller working in the approximate domain and a robust controller working outside the approximate domain, in addition, a new smooth switching function is designed to achieve the smooth switching between the two controllers, in order to ensure the globally uniformly ultimately bounded of all closed-loop signals. The Lyapunov analysis method is used to strictly prove the global stability under the combined action of unmeasured states and system uncertainties, and the output tracking error is guaranteed to converge to an arbitrarily small neighborhood through a reasonable selection of design parameters. A numerical example and a practical example were put forward to verify the effectiveness of the control strategy.
- Subjects :
- Lyapunov function
Artificial neural network
Computer Networks and Communications
Computer science
Stability (learning theory)
Function (mathematics)
Computer Science Applications
Tracking error
symbols.namesake
Nonlinear system
Artificial Intelligence
Control theory
Bounded function
symbols
Software
Subjects
Details
- ISSN :
- 21622388 and 2162237X
- Volume :
- 34
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Neural Networks and Learning Systems
- Accession number :
- edsair.doi.dedup.....d3ec225382c2701a0d43f3b027b85768