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Stability Analysis and Hadamard Synergic Control for a Class of Dynamical Networks

Stability Analysis and Hadamard Synergic Control for a Class of Dynamical Networks
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摘要 Hadamard synergic control is a new kind of control problem which is achieved via a composite strategy of the state feedback control and the direct regulation of the part of connection coefficients of system state variables. Such a control is actually used very often in the practical areas. In this paper, we discuss Hadamard synergic stabilization problem for a class of dynamical networks. We analyze three cases: 1) Synergic stabilization problem for the general twonodenetwork. 2) Synergic stabilization problem for a special kind of networks. 3) Synergic stabilization problem for special kind of networks with communication timedelays. The mechanism of the synergic action between two control strategies: feedback control and the connection coefficients regulations are presented. Hadamard synergic control is a new kind of control problem which is achieved via a composite strategy of the state feedback control and the direct regulation of the part of connection coefficients of system state variables. Such a control is actually used very often in the practical areas. In this paper, we discuss Hadamard synergic stabilization problem for a class of dynamical networks. We analyze three cases: 1) Synergic stabilization problem for the general twonodenetwork. 2) Synergic stabilization problem for a special kind of networks. 3) Synergic stabilization problem for special kind of networks with communication timedelays. The mechanism of the synergic action between two control strategies: feedback control and the connection coefficients regulations are presented.
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出处 《Intelligent Control and Automation》 2010年第1期36-47,共12页 智能控制与自动化(英文)
关键词 HADAMARD Synergic CONTROL Algebraically Graph Theory DECENTRALIZED Feedback CONTROL Connection COEFFICIENT GAIN Matrix Hadamard Synergic Control Algebraically Graph Theory Decentralized Feedback Control Connection Coefficient Gain Matrix
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