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Sliding mode control-based fixed-time stabilization and synchronization of inertial neural networks with time-varying delays
- Source :
- Neural Computing and Applications. 33:11555-11572
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- In this article, we are interested in the fixed-time stabilization (FTSt) and fixed-time synchronization (FTSy) of a class of inertial neural networks with time-varying and distributed delays. To obtain FTSt and FTSy, sliding mode controllers are developed based on sliding mode control techniques and by using sliding variables. Two polynomial feedback control laws are exploited to achieve the FTSt and the FTSy but they are singular. To get rid of the singularities, the saturation function is used into the design of the controllers and the almost FTSt and almost FTSy are proved. Finally, numerical examples are presented to show the effectiveness of the theoretical results.
- Subjects :
- 0209 industrial biotechnology
Polynomial
Inertial frame of reference
Artificial neural network
Computer science
Mode (statistics)
02 engineering and technology
Function (mathematics)
Sliding mode control
Synchronization
020901 industrial engineering & automation
Artificial Intelligence
Control theory
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Gravitational singularity
Software
Subjects
Details
- ISSN :
- 14333058 and 09410643
- Volume :
- 33
- Database :
- OpenAIRE
- Journal :
- Neural Computing and Applications
- Accession number :
- edsair.doi...........e0e323b4e15b7863bf034089b32f6cfd