Continuification control of large-scale multiagent systems under limited sensing and structural perturbations

Abstract

We investigate the stability and robustness properties of a continuification-based strategy for the control of large-scale multiagent systems. Within continuation-based strategy, one transforms the microscopic, agent-level description of the system dynamics into a macroscopic continuum-level, for which a control action can be synthesized to steer the macroscopic dynamics towards a desired distribution. Such an action is ultimately discretized to obtain a set of deployable control inputs for the agents to achieve the goal. The mathematical proof of convergence toward the desired distribution typically relies on the assumptions that no disturbance is present and that each agent possesses global knowledge of all the others' positions. Here, we analytically and numerically address the possibility of relaxing these assumptions for the case of a one-dimensional system of agents moving in a ring. We offer compelling evidence in favor of the use of a continuification-based strategy when agents only possess a finite sensing capability and spatio-temporal perturbations affect the macroscopic dynamics of the ensemble. We also discuss some preliminary results about the role of an integral action in the macroscopic control solution.

References in corpus (4)

• PDE-based multi-agent formation control using flatness and backstepping: analysis, design and robot experiments
• Functional Control of Oscillator Networks
• A Continuation Method for Large-Scale Modeling and Control: from ODEs to PDE, a Round Trip
• Continuification-based control of large multiagent systems in a ring

Cited by in corpus (4)

• Leader-Follower Density Control of Spatial Dynamics in Large-Scale Multi-Agent Systems
• Distributed control for geometric pattern formation of large-scale multirobot systems
• Decentralized Continuification Control of Multi-Agent Systems via Distributed Density Estimation
• High-dimensional continuification control of large-scale multi-agent systems under limited sensing and perturbations