Title :
Adaptive Pinning Control of Deteriorated Nonlinear Coupling Networks With Circuit Realization
Author :
Xiao-Zheng Jin ; Guang-Hong Yang ; Wei-Wei Che
Author_Institution :
Key Lab. of Manuf. Ind. Integrated Autom., Shenyang Univ., Shenyang, China
Abstract :
This paper deals with a class of complex networks with nonideal coupling networks, and addresses the problem of asymptotic synchronization of the complex network through designing adaptive pinning control and coupling adjustment strategies. A more general coupled nonlinearity is considered as perturbations of the network, while a serious faulty network named deteriorated network is also proposed to be further study. For the sake of eliminating these adverse impacts for synchronization, indirect adaptive schemes are designed to construct controllers and adjusters on pinned nodes and nonuniform couplings of un-pinned nodes, respectively. According to Lyapunov stability theory, the proposed adaptive strategies are successful in ensuring the achievement of asymptotic synchronization of the complex network even in the presence of perturbed and deteriorated networks. The proposed schemes are physically implemented by circuitries and tested by simulation on a Chua´s circuit network.
Keywords :
Chua´s circuit; Lyapunov methods; adaptive control; asymptotic stability; complex networks; control nonlinearities; control system synthesis; perturbation techniques; synchronisation; Chua´s circuit network; Lyapunov stability theory; adaptive pinning control design; adjusters; asymptotic synchronization; circuit realization; complex networks; controller; coupled nonlinearity; coupling adjustment strategies; deteriorated networks; deteriorated nonlinear coupling networks; indirect adaptive schemes; nonuniform couplings; perturbed networks; pinned nodes; unpinned nodes; Adaptive systems; Biological neural networks; Chaos; Complex networks; Couplings; Synchronization; Vectors; Adaptive pinning control; circuit realization; network deterioration; nonlinear coupling networks; synchronization;
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
DOI :
10.1109/TNNLS.2012.2202246