Practical Resource Allocation of Networked Euler-Lagrange Agents With Quantized-Data Interactions and Arbitrary Bounded Uncertainties
Teng‐Fei Ding, Song Li-ping, Ming‐Feng Ge, Zhi‐Wei Liu, Ming Chi · IEEE Transactions on Network Science and Engineering · 2023
This article investigates the practical resource allocation problem of networked Euler-Lagrange agents (NELAs) with quantized-data interactions and arbitrary bounded uncertainties. A novel hierarchical resource allocation control (HRAC) algorithm is developed including the distributed resource allocation estimator and adaptive local controller. Specifically, the distributed resource allocation estimator is designed based on the gradient descent and state feedback such that the estimated states achieve the optimal resource allocation. Further, the adaptive local controller is designed without using the upper boundary of the total disturbances such that the states of the NELAs are forced to track the optimal values while the global cost function is the minimum, where the global cost function is the sum of local cost function. Several sufficient conditions are established with the help of Lyapunov stability argument and adaptive control theory. Finally, the effectiveness of the proposed algorithm is verified by the two simulation examples.