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Finite-Horizon ${\mathcal H}_{\infty }$ Consensus Control of Time-Varying Multiagent Systems With Stochastic Communication Protocol

This paper is concerned with the distributed ${\mathcal {H}}_{\infty }$ consensus control problem for a discrete time-varying multiagent system with the stochastic communication protocol (SCP). A directed graph is used… Click to show full abstract

This paper is concerned with the distributed ${\mathcal {H}}_{\infty }$ consensus control problem for a discrete time-varying multiagent system with the stochastic communication protocol (SCP). A directed graph is used to characterize the communication topology of the multiagent network. The data transmission between each agent and the neighboring ones is implemented via a constrained communication channel where only one neighboring agent is allowed to transmit data at each time instant. The SCP is applied to schedule the signal transmission of the multiagent system. A sequence of random variables is utilized to capture the scheduling behavior of the SCP. By using the mapping technology combined with the Hadamard product, the closed-loop multiagent system is modeled as a time-varying system with a stochastic parameter matrix. The purpose of the addressed problem is to design a cooperative controller for each agent such that, for all probabilistic scheduling behaviors, the ${\mathcal {H}}_{\infty }$ consensus performance is achieved over a given finite horizon for the closed-loop multiagent system. A necessary and sufficient condition is derived to ensure the $ {\mathcal {H}}_{ \infty }$ consensus performance based on the completing squares approach and the stochastic analysis technique. Then, the controller parameters are obtained by solving two coupled backward recursive Riccati difference equations. Finally, a numerical example is given to illustrate the effectiveness of the proposed controller design scheme.

Keywords: consensus; mathcal infty; time; tex math; inline formula

Journal Title: IEEE Transactions on Cybernetics
Year Published: 2017

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