Guaranteed Cost Finite-Time Control of Uncertain Coupled Neural Networks

This article investigates a robust guaranteed cost finite-time control for coupled neural networks with parametric uncertainties. The parameter uncertainties are assumed to be time-varying norm bounded, which appears on the system state and input matrices. The robust guaranteed cost control laws pre...

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Published inIEEE transactions on cybernetics Vol. 52; no. 1; pp. 481 - 494
Main Authors Mei, Jun, Lu, Zhenyu, Hu, Junhao, Fan, Yuling
Format Journal Article
LanguageEnglish
Published United States IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptive control
Algorithms
Control systems
Control theory
Convergence
Cost function
Feedback
Feedback control
Finite-time synchronization
guaranteed cost control
intermittent control
Linear matrix inequalities
Mathematical analysis
Neural networks
Neural Networks, Computer
Optimization
Parameter uncertainty
Robust control
Synchronization
Time synchronization
uncertain coupled neural networks
Upper bound
Upper bounds
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Abstract This article investigates a robust guaranteed cost finite-time control for coupled neural networks with parametric uncertainties. The parameter uncertainties are assumed to be time-varying norm bounded, which appears on the system state and input matrices. The robust guaranteed cost control laws presented in this article include both continuous feedback controllers and intermittent feedback controllers, which were rarely found in the literature. The proposed guaranteed cost finite-time control is designed in terms of a set of linear-matrix inequalities (LMIs) to steer the coupled neural networks to achieve finite-time synchronization with an upper bound of a guaranteed cost function. Furthermore, open-loop optimization problems are formulated to minimize the upper bound of the quadratic cost function and convergence time, it can obtain the optimal guaranteed cost periodically intermittent and continuous feedback control parameters. Finally, the proposed guaranteed cost periodically intermittent and continuous feedback control schemes are verified by simulations.
AbstractList This article investigates a robust guaranteed cost finite-time control for coupled neural networks with parametric uncertainties. The parameter uncertainties are assumed to be time-varying norm bounded, which appears on the system state and input matrices. The robust guaranteed cost control laws presented in this article include both continuous feedback controllers and intermittent feedback controllers, which were rarely found in the literature. The proposed guaranteed cost finite-time control is designed in terms of a set of linear-matrix inequalities (LMIs) to steer the coupled neural networks to achieve finite-time synchronization with an upper bound of a guaranteed cost function. Furthermore, open-loop optimization problems are formulated to minimize the upper bound of the quadratic cost function and convergence time, it can obtain the optimal guaranteed cost periodically intermittent and continuous feedback control parameters. Finally, the proposed guaranteed cost periodically intermittent and continuous feedback control schemes are verified by simulations.
Author Hu, Junhao
Fan, Yuling
Lu, Zhenyu
Mei, Jun
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Snippet This article investigates a robust guaranteed cost finite-time control for coupled neural networks with parametric uncertainties. The parameter uncertainties...
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SubjectTerms Adaptive control
Algorithms
Control systems
Control theory
Convergence
Cost function
Feedback
Feedback control
Finite-time synchronization
guaranteed cost control
intermittent control
Linear matrix inequalities
Mathematical analysis
Neural networks
Neural Networks, Computer
Optimization
Parameter uncertainty
Robust control
Synchronization
Time synchronization
uncertain coupled neural networks
Upper bound
Upper bounds
Title Guaranteed Cost Finite-Time Control of Uncertain Coupled Neural Networks
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https://www.ncbi.nlm.nih.gov/pubmed/32275628
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