Coalitional Zero-Sum Games for ${H_{\infty}}$ Leader-Following Consensus Control

TL;DR

This paper develops a game-theoretic approach for robust leader-following consensus in multi-agent systems under adversarial attacks, using decentralized algorithms to solve complex Riccati equations.

Contribution

It introduces a novel coalitional zero-sum game framework for $H_$ control, with decentralized methods for high-dimensional Riccati equations.

Findings

The proposed algorithms effectively achieve robust consensus under attacks.

Decentralized computation reduces complexity of high-dimensional Riccati equations.

Numerical simulations validate the control strategy's robustness and effectiveness.

Abstract

This paper investigates the leader-following consensus problem for a class of multi-agent systems subject to adversarial attack-like external inputs. To address this, we formulate the robust leader-following control problem as a global coalitional min-max zero-sum game using differential game theory. Specifically, the agents' control inputs form a coalition to minimize a global cost function, while the attacks form an opposing coalition to maximize it. Notably, when these external adversarial attacks manifest as disturbances, the designed game-theoretic control policy systematically yields a robust $H_\infty$ control law. Addressing this problem inherently requires solving a high-dimensional generalized algebraic Riccati equation (GARE), which poses significant challenges for distributed computation and controller implementation. To overcome these challenges, we propose a two-fold approach. First, a decentralized computational strategy is devised to decompose the high-dimensional GARE into multiple uniform, lower-dimensional GAREs. Second, a dynamic average consensus-based decoupling algorithm is developed to resolve the inherent coupling structure of the robust control law, thereby facilitating its distributed implementation. Finally, numerical simulations on the formation control of multi-vehicle systems with feedback-linearized dynamics are conducted to validate the effectiveness of the proposed algorithms.