Multi-Agent Learning for Resilient Distributed Control Systems

TL;DR

This paper discusses a multi-agent system framework for large-scale distributed control systems, emphasizing the role of AI and learning algorithms in enhancing system resilience against disturbances and threats.

Contribution

It introduces a novel MAS framework incorporating AI stacks and learning algorithms for resilient distributed control, with a case study on renewable energy systems.

Findings

Distributed learning algorithms enable subsystems to coordinate responses.

Game-theoretic learning improves response to adversarial behaviors.

The MAS architecture enhances resilience in renewable energy systems.

Abstract

Resilience describes a system's ability to function under disturbances and threats. Many critical infrastructures, including smart grids and transportation networks, are large-scale complex systems consisting of many interdependent subsystems. Decentralized architecture becomes a key resilience design paradigm for large-scale systems. In this book chapter, we present a multi-agent system (MAS) framework for distributed large-scale control systems and discuss the role of MAS learning in resiliency. This chapter introduces the creation of an artificial intelligence (AI) stack in the MAS to provide computational intelligence for subsystems to detect, respond, and recover. We discuss the application of learning methods at the cyber and physical layers of the system. The discussions focus on distributed learning algorithms for subsystems to respond to each other, and game-theoretic learning for them to respond to disturbances and adversarial behaviors. The book chapter presents a case study of distributed renewable energy systems to elaborate on the MAS architecture and its interface with the AI stack.