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Adaptive Neural Network Finite-Time Dynamic Surface Control for Nonlinear Systems.
- Source :
-
IEEE Transactions on Neural Networks & Learning Systems . Dec2021, Vol. 32 Issue 12, p5688-5697. 10p. - Publication Year :
- 2021
-
Abstract
- This article addresses the problem of finite-time neural network (NN) adaptive dynamic surface control (DSC) design for a class of single-input single-output (SISO) nonlinear systems. Such designs adopt NNs to approximate unknown continuous system functions. To avoid the “explosion of complexity” problem, a novel nonlinear filter is developed in control design. Under the framework of adaptive backstepping control, an NN adaptive finite-time DSC design algorithm is proposed by adopting a smooth projection operator and finite-time Lyapunov stable theory. The developed control algorithm means that the tracking error converges to a small neighborhood of origin within finite time, which further verifies that all the signals of the controlled system possess globally finite-time stability (GFTS). Finally, both numerical and practical simulation examples and comparing results are provided to elucidate the superiority and effectiveness of the proposed control algorithm. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 32
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 153925401
- Full Text :
- https://doi.org/10.1109/TNNLS.2020.3027335