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Distributed Model Predictive Control for Tracking Consensus of Linear Multiagent Systems With Additive Disturbances
Abstract:
In this article, we investigate a robust distributed model predictive control (DMPC) scheme for tracking the consensus of linear multiagent systems (MASs) subject to additive disturbances and time-varying communication delays. A terminal constraint set is constructed by the Lyapunov-Razumikhin functional, and a corresponding local controller is designed for each agent. Furthermore, the sufficient conditions ensure that the terminal constraint set is provided in the form of linear matrix inequalities (LMIs). The recursive feasibility of the proposed algorithm is guaranteed based on the designed terminal constraint set, terminal cost, and local controller. Moreover, the closed-loop system is shown to be input-to-state stable (ISS). An illustrative example is given to verify the effectiveness of the presented approach.
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