Finite-iteration learning tracking of multi-agent systems via the distributed optimization method

摘要

In this paper, the finite-iteration tracking issue of multi-agent systems is investigated with the distributed optimization method. Since the infinite-iteration strategy is unrealistic, the finite-iteration approach is adopted to study the problem. Moreover, the tracking performance is undesired for the traditional iterative learning schemes. Then optimization technique is used to cover the drawback. The tracking error is regarded as the optimization objective, and the consensus tracking issue is considered as the distributed optimization problem. An appropriate learning control strategy is designed on the basis of the gradient of the tracking error. Further, the optimization issue can be solved by means of the finite iteration approach, and the learning algorithm can improve the tracking performance. Theoretical results are proposed by using the norm estimation and some inequalities skills. At last, a numerical simulation is given to illustrate the effectiveness of the main results.(c) 2021 Published by Elsevier B.V.