Cooperative optimal output regulation of multi-agent systems using adaptive dynamic programming

This paper proposes a novel solution to the adaptive optimal output regulation problem of continuoustime linear multi-agent systems. A key strategy is to resort to reinforcement learning and approximate/adaptive dynamic programming. A data-driven, non-model-based algorithm is given to design a distributed adaptive suboptimal output regulator in the presence of unknown system dynamics. The effectiveness of the proposed computational control algorithm is demonstrated via cooperative adaptive cruise control of connected and autonomous vehicles.