Abstract
A distributed resource allocation problem of high-order systems is studied in this article, where the decisions of agents are constrained by network resources. In contrast to well-known resource allocation problems, this problem involves the high-order dynamics of agents, which results in the ineffectiveness of existing distributed resource allocation algorithms. In order to optimally allocate the network resource, a distributed algorithm is developed via state feedback and gradient descent. Moreover, the convergence of the algorithm is analyzed with the help of convex analysis and Lyapunov stability theory. By the algorithm, the high-order agents globally exponentially converge to the optimal solution. Finally, the algorithm is illustrated by numerical examples.
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