This paper focuses on the distributed optimal cooperative control for continuous-time nonlinear multi-agent systems (MASs) with completely unknown dynamics via adaptive dynamic programming (ADP) technology. By introducing predesigned extra compensators, the augmented neighborhood error systems are derived which successfully circumvents the system knowledge requirement for ADP. It is revealed that the optimal consensus protocols actually work as solutions of the MAS differential game. Policy iteration (PI) algorithm is adopted, and it is theoretically proved that the iterative value function sequence strictly converges to the solution of the coupled Hamilton-Jacobi-Bellman (CHJB) equation. Based on this point, a novel online iterative scheme is proposed which runs based on the data sampled from the augmented system and the gradient of the value function. Neural networks (NNs) are employed to implement the algorithm and the weights are updated, in the least-square sense, to the ideal val 查看全文>>