On Scalable Supervisory Control of Multi-Agent Discrete-Event Systems

TL;DR

This paper introduces a scalable supervisory control method for multi-agent discrete-event systems that maintains efficiency regardless of system size, enabling robust and distributed control architectures.

Contribution

It presents a novel relabeling-based approach to synthesize scalable supervisors and local controllers, ensuring invariance and efficiency in large multi-agent systems.

Findings

Supervisors are independent of the number of agents, reducing computational complexity.

The method supports dynamic addition or removal of agents without re-synthesis.

Distributed control architecture is achieved through scalable local controllers.

Abstract

In this paper we study multi-agent discrete-event systems where the agents can be divided into several groups, and within each group the agents have similar or identical state transition structures. We employ a relabeling map to generate a "template structure" for each group, and synthesize a scalable supervisor whose state size and computational process are independent of the number of agents. This scalability allows the supervisor to remain invariant (no recomputation or reconfiguration needed) if and when there are agents removed due to failure or added for increasing productivity. The constant computational effort for synthesizing the scalable supervisor also makes our method promising for handling large-scale multi-agent systems. Moreover, based on the scalable supervisor we design scalable local controllers, one for each component agent, to establish a purely distributed control architecture. Three examples are provided to illustrate our proposed scalable supervisory synthesis and the resulting scalable supervisors as well as local controllers.