Data-Driven Optimal Synchronization for Complex Networks With Unknown Dynamics

This paper studies the data-driven optimal synchronization problem for complex networks (CNs) with unknown dynamics. By using pre-compensation technology, a compensator and a controller are proposed. Then, an augmented error system is constructed, which can circumvent the requirement of system dynamics. It is revealed that the the optimal synchronization control of CNs works as the optimal regulation of the augmented system with a performance function. A novel policy iteration (PI) algorithm is given to ensure that the iterative performance function can converge to the optimal value which is the solution of the coupled Hamilton-Jacobi-Bellman equation (HJB), which means that the optimal regulation of the augmented system can be solved and the synchronization can be achieved. Based on this, a novel data-driven control scheme is proposed, which is composed of parts: compensator, controller and critic network. The iterative performance is generated by critic network. The compensator is used to construct the control parameter by using performance and the controller is used to construct control input by using control parameter. Both compensator and critic network are implemented by neural networks (NNs) and only depend on the process sampling data. Finally, we use robot network as an example to verify the effectiveness of proposed control scheme.