New Results in Cooperative Adaptive Optimal Output Regulation

This paper investigates the cooperative adaptive optimal output regulation problem of continuous-time linear multi-agent systems. As the multi-agent system dynamics are uncertain, solving regulator equations and the corresponding algebraic Riccati equations is challenging, especially for high-order systems. In this paper, a novel method is proposed to approximate the solution of regulator equations, i.e., gradient descent method. It is worth noting that this method obtains gradients through online data rather than model information. A data-driven distributed adaptive suboptimal controller is developed by adaptive dynamic programming, so that each follower can achieve asymptotic tracking and disturbance rejection. Finally, the effectiveness of the proposed control method is validated by simulations.