Output synchronization of a class of complex dynamic networks: A reinforcement learning method

In this paper, to achieve the synchronization control for a class of complex dynamic networks with completely unknown system dynamics, a reinforcement learning output feedback algorithm based on state reconstruction is proposed. Given the high cost and complexity associated with obtaining the full state information, an output-based node state reconstruction method is employed for the first time in complex dynamic networks. The proposed method utilizes a sequence composed of a finite number of output data to reconstruct the current state. At the same time, the overall error system is constructed to handle the coupling relationship between nodes, to facilitate the controller design. Thereafter, considering the system dynamics are unknown, an algorithm based on reinforcement learning is proposed to ensure rapid synchronization of node outputs, and the convergence of proposed method is proven. Finally, the feasibility of proposed algorithm is corroborated through a simulation example and a multi-vehicle system.