Data-Driven Decentralized Control for Large-Scale Systems With Sparsity and Communication Delays

This article studies the decentralized control of large-scale systems with sparsity and communication delays. The large-scale system is defined over a directed connected graph and the information structure is partially nested. Based on the decomposition of the noise history, the optimal problem of the overall large-scale system can be decomposed into independent subproblems. Hence, the data-driven decentralized control method is investigated to find the optimal controllers using adaptive dynamic programming (ADP), which could release the dependence on the knowledge of model. In addition, state feedback and output feedback policy iteration algorithms are developed, respectively. Rigorous stability analysis shows that the proposed algorithms can stabilize the large-scale systems asymptotically. Finally, the effectiveness of the proposed theoretical methods is demonstrated by the application of heavy duty vehicle (HDV) platooning.