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Neural-network-based boundary control for a gantry crane system with unknown friction and output constraint.
- Source :
-
Neurocomputing . Jan2023, Vol. 518, p271-281. 11p. - Publication Year :
- 2023
-
Abstract
- This paper presents a neural-network-based boundary control method for a gantry system with unknown friction and output constraint. Firstly, to tackle the unknown friction, a radial basis function neural network (RBFNN) is adopted to approximate it. Secondly, we employ a barrier Lyapunov function to handle the output constraint problem. Then, a neural-network-based boundary controller is proposed to deal with the aforementioned problems. Subsequently, based on the Lyapunov stability approach, the uniformly ultimately bounded stability of the state of the closed-loop system is guaranteed. Finally, the effectiveness of the developed control method is illustrated through both numerical simulations and physical experiments. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 518
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 160438142
- Full Text :
- https://doi.org/10.1016/j.neucom.2022.11.010