2022 journal article

Learning Distributed Stabilizing Controllers for Multi-Agent Systems


By: G. Jing n, H. Bai*, J. George*, A. Chakrabortty n & P. Sharma*

co-author countries: United States of America 🇺🇸
author keywords: Heuristic algorithms; Multi-agent systems; Power system dynamics; Approximation algorithms; Damping; System dynamics; Regulators; Reinforcement learning; linear quadratic regulator; optimal distributed control; multi-agent systems
Source: Web Of Science
Added: July 19, 2021

We address model-free distributed stabilization of heterogeneous continuous-time linear multi-agent systems using reinforcement learning (RL). Two algorithms are developed. The first algorithm solves a centralized linear quadratic regulator (LQR) problem without knowing any initial stabilizing gain in advance. The second algorithm builds upon the results of the first algorithm, and extends it to distributed stabilization of multi-agent systems with predefined interaction graphs. Rigorous proofs are provided to show that the proposed algorithms achieve guaranteed convergence if specific conditions hold. A simulation example is presented to demonstrate the theoretical results.