This paper addresses the constrained distributed optimization problem of heterogeneous linear multiagent systems, where the agents with linear dynamics are subject to local set constraints, global nonlinear inequality...
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This paper addresses the constrained distributed optimization problem of heterogeneous linear multiagent systems, where the agents with linear dynamics are subject to local set constraints, global nonlinear inequality constraints and heterogeneous communication delays. Agents collaborate to minimize a global objective function by communicating with their neighbors in a graph. Each agent's decision variable is constrained in a local set. The decision variables of all agents are coupled by global inequality constraints which are modeled by nonlinear functions. To handle the heterogeneous constant communication delays, the scattering transformation between neighbors is employed. We design a new distributed control law to investigate the passivity of systems of individual agents in the presence of constraints and communication delays. Integrating the proposed control law with the scattering transformation, we prove that systems converge to the optimal solution which minimizes the global objective functions. Simulations of heterogeneous linear multi-agent systems are presented to illustrate the effectiveness of the distributed control law. (C) 2021 Elsevier B.V. All rights reserved.
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