Optimal output regulation of heterogeneous multi-agent systems via reinforcement learning
XIONG Chun-ping
MA Qian
Abstract：The optimal output regulation of heterogeneous multi-agent systems is investigated in this paper.A directed spanning tree is contained in the communication network.First of all,the exo-system state compensator and the state feedback controller are designed.Based on the graph theory and the Lyapunov stability theory,it is proved that the de-signed compensator and controller can achieve the general output regulation.Then,the optimal output regulation problem is worked out via minimizing a predefined cost function.Combining optimal control theory with reinforcement learning technology,two algorithms are proposed to deal with the optimal controller,which are model-based policy iteration algo-rithm and model-free off-policy algorithm.The process of obtaining the optimal controller by model-free algorithm does not need to solve the output regulation equation or use the information of system dynamics.Last but not least,a numerical example is proposed to verify the effectiveness of the proposed algorithms.
Keywords：heterogeneous multi-agent systemsoptimal output regulationpolicy iterationmodel-free algorithmreinforcement learning
Publication Date：2025-03-31
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 491-498 )