Cooperative adaptive optimal output regulation of multi-agent systems with an application to connected and autonomous vehicles

This paper proposes a novel control approach based on gradient descent methods to solve the problem of cooperative adaptive optimal output regulation of continuous-time linear multi-agent systems. This proposed approach calculates gradients through online data rather than model information, such that a data-driven distributed adaptive controller is developed by adaptive dynamic programming, which ensures that each follower can achieve asymptotic tracking and disturbance rejection. Finally, the effectiveness of the proposed control algorithm is verified by simulation of connected and autonomous vehicles.