This paper studies an optimal consensus tracking problem of heterogeneous linear multiagent systems. By introducing tracking error dynamics, the optimal tracking problem is reformulated as finding a Nash-equilibrium solution to multiplayer games, which can be done by solving associated coupled Hamilton-Jacobi equations. A data-based error estimator is designed to obtain the data-based control for the multiagent systems. Using the quadratic functional to approximate every agent's value function, we can obtain the optimal cooperative control by the input-output (I/O) Q-learning algorithm with a value iteration technique in the least-square sense. The control law solves the optimal consensus problem for multiagent systems with measured I/O information, and does not rely on the model of multiagent systems. A numerical example is provided to illustrate the effectiveness of the proposed algorithm. Index Terms-Consensus, data-based control, optimal cooperative control, reinforcement learning. Manuscript
Zhang REF studied an optimal consensus tracking problem of heterogeneous linear multiagent systems, and found a Nashequilibrium solution by solving associated coupled Hamilton-Jacobi equations.
Data-Based Optimal Control of Multiagent Systems: A Reinforcement Learning Design Approach
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics
Computer Science Mathematics Medicine