학술논문

Optimal Consensus Control for Multi-Agent Systems With Unknown Dynamics and States of Leader: A Distributed KREM Learning Method
Document Type
Periodical
Source
IEEE Transactions on Circuits and Systems II: Express Briefs IEEE Trans. Circuits Syst. II Circuits and Systems II: Express Briefs, IEEE Transactions on. 71(4):2219-2223 Apr, 2024
Subject
Components, Circuits, Devices and Systems
Observers
Consensus control
Artificial neural networks
Sensors
Optimal control
Multi-agent systems
Lyapunov methods
Optimal consensus control
multi-agent systems
unknown dynamics
neural networks
Kreisselmeiers regressor extension and mixing
Language
ISSN
1549-7747
1558-3791
Abstract
This brief addresses the optimal consensus control problem of a class of nonlinear leader-follower multi-agent systems (MASs), where the dynamics and states of the leader are unknown. A distributed Kreisselmeiers Regressor Extension and Mixing (KREM)-based control scheme is developed. Specifically, a distributed parameter estimation-based observer is first proposed, which can estimate not only the dynamic parameters but also the states of the leader for each agent. This allows to transform the optimal consensus control problem into an optimal tracking control problem of the leader’s state. To solve the optimization problem, an only-critic learning structure is proposed, incorporating a novel adaptive tuning rule for the critic networks using the KREM technique to learn the unknown network weights. The system stability of the closed-loop MASs and the fast convergence of the distributed parameter estimation-based observer are conducted based on the Lyapunov stability theory. The effectiveness of the proposed control method is validated via a numerical simulation.