Neural-Network-Based Adaptive Constrained Control for Switched Systems Under State-Dependent Switching Law

This article addresses the adaptive tracking control problem for switched uncertain nonlinear systems with state constraints via the multiple Lyapunov function approach. The system functions are considered unknown and approximated by radial basis function neural networks (RBFNNs). For the state cons...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2023-08, Vol.34 (8), p.4057-4067
Main Authors: Tang, Li, Zhang, Xin-Yu, Liu, Yan-Jun, Tong, Shaocheng
Format: Article
Language:English
Subjects:
Adaptive control
Constrained control
Constraints
Control systems
Liapunov functions
Lyapunov methods
Neural networks
neural networks (NNs)
Nonlinear systems
Radial basis function
Reference signals
state-dependent switching law
Subsystems
switched nonlinear systems
Switched systems
Switches
Switching
Tracking control
Uncertainty
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Summary:This article addresses the adaptive tracking control problem for switched uncertain nonlinear systems with state constraints via the multiple Lyapunov function approach. The system functions are considered unknown and approximated by radial basis function neural networks (RBFNNs). For the state constraint problem, the barrier Lyapunov functions (BLFs) are chosen to ensure the satisfaction of the constrained properties. Moreover, a state-dependent switching law is designed, which does not require stability for individual subsystems. Then, using the backstepping technique, an adaptive NN controller is constructed such that all signals in the resulting system are bounded, the system output can track the reference signal to a compact set, and the constraint conditions for states are not violated under the designed state-dependent switching signal. Finally, simulation results show the effectiveness of the proposed method.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2021.3120999