Adaptive Neural Finite-Time Control of Non-Strict Feedback Nonlinear Systems With Non-Symmetrical Dead-Zone

The control design method for a class of non-strict feedback nonlinear systems is studied in this brief considering uncertain nonlinearities and unknown non-symmetrical input dead-zone. Combining with the finite-time command filtered backstepping (FCFB) technique, a novel finite-time adaptive contro...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2024-01, Vol.35 (1), p.1409-1414
Main Authors: Cai, Mingjie, Shi, Peng, Yu, Jinpeng
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive neural network control
Backstepping
command filter
Control design
Convergence
Explosions
Feedback
finite-time convergence
Learning systems
Mathematical models
Neural networks
non-strict feedback
Nonlinear systems
Nonlinearity
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Summary:The control design method for a class of non-strict feedback nonlinear systems is studied in this brief considering uncertain nonlinearities and unknown non-symmetrical input dead-zone. Combining with the finite-time command filtered backstepping (FCFB) technique, a novel finite-time adaptive control approach is proposed in which a neural network-based methodology is adopted to cope with the uncertain nonlinearities in the non-strict feedback form. The input dead-zone model is transformed into a simple linear system with unknown gain and bounded disturbance which is estimated by an adaptive factor. Using the finite-time Lyapunov theory, the system convergence is proved. And the effectiveness of the proposed control scheme is verified through comparative numerical simulations.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2022.3178366