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Adaptive neural network control of second-order underactuated systems with prescribed performance constraints.
- Source :
-
International Journal of Nonlinear Sciences & Numerical Simulation . Feb2023, Vol. 24 Issue 1, p81-93. 13p. - Publication Year :
- 2023
-
Abstract
- This paper studies the trajectory tracking control problem of second-order underactuated system subject to system uncertainties and prescribed performance constraints. By combining radial basis function neural networks (RBFNNs) with input–output linearization methods, an adaptive neural network-based control approach is proposed and the adaptive laws are given through Lyapunov method and Taylor expansion linearization approach. The main contributions of this paper are that: (1) by introducing weight performance function and transformation function, the states never violate the prescribed performance constraints; (2) the control scheme takes the unknown control gain direction into consideration and the singular problem of control design can be avoided; (3) through rigorously stability analysis, all signal of closed-loop system are proved to be uniformly ultimately bounded. The effectiveness of the proposed control scheme was verified by comparative simulation. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15651339
- Volume :
- 24
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- International Journal of Nonlinear Sciences & Numerical Simulation
- Publication Type :
- Academic Journal
- Accession number :
- 162435287
- Full Text :
- https://doi.org/10.1515/ijnsns-2020-0141