In this paper, the optimal adaptive leader-follower consensus of linear continuous time multi-agent systems is considered. The error dynamics of each player depends on its neighbors’ information. Detailed analysis of online optimal leader-follower consensus under known and unknown dynamics is presented. The introduced reinforcement learning-based algorithms learn online the approximate solution to algebraic Riccati equations. An optimal adaptive control technique is employed to iteratively solve the algebraic Riccati equation based on the online measured error state and input information for each agent without requiring the priori knowledge of the system matrices. The decoupling of the multi-agent system global error dynamics facilitates the employment of policy iteration and optimal adaptive control techniques to solve the leader follower consensus problem under known and unknown dynamics. Simulation results verify the effectiveness of the proposed methods.

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