Distributed Coordination for a Class of Nonlinear Multi-agent Systems with Regulation Constraints
For researchers in multi-agent systems and distributed control, this work extends leader-following coordination to cases where the reference is not given by a leader but by a distributed optimization problem with noisy constraints.
This paper addresses multi-agent coordination with steady-state regulation constraints for nonlinear systems, where the reference signal is the optimal solution of a distributed optimization problem with noisy data. The proposed passivity-based distributed algorithms achieve optimal steady-state regulation while rejecting unknown observation disturbances.
In this paper, a multi-agent coordination problem with steady-state regulation constraints is investigated for a class of nonlinear systems. Unlike existing leader-following coordination formulations, the reference signal is not given by a dynamic autonomous leader but determined as the optimal solution of a distributed optimization problem. Furthermore, we consider a global constraint having noisy data observations for the optimization problem, which implies that reference signal is not trivially available with existing optimization algorithms. To handle those challenges, we present a passivity-based analysis and design approach by using only local objective function, local data observation and exchanged information from their neighbors. The proposed distributed algorithms are shown to achieve the optimal steady-state regulation by rejecting the unknown observation disturbances for passive nonlinear agents, which are persuasive in various practical problems. Applications and simulation examples are then given to verify the effectiveness of our design.