Collective point-to-point iterative learning control of multi-agent system with switched reference

Multi-agent system (MAS) can accomplish complex control tasks through independent decision making and collaboration among each individual. Iterative learning control (ILC), as a high-performance intelligent control strategy, is widely used in multi-agent systems. Among the multi-agent control tasks, there is a kind of task called point-to-point tracking, which only needs to consider the reference of some specific time points. Previous studies on point-to-point iterative learning control (P2PILC) of MAS are all aimed at collaborative tasks. However, independent point-to-point control tasks have not been studied. In this article, to realize the complementation of individual performance, a collective point-to-point iterative learning controller is designed through collective intelligence. In addition, reference often switched with batches in practice, so it introduces switched reference and designs corresponding iterative learning control switching strategies at switching batch. Finally, the effectiveness of the proposed algorithm is verified by a simulation example of multi-manipulator picking and placing operation.(c) 2023 The Franklin Institute. Published by Elsevier Inc. All rights reserved.

Automated summary

This article proposes a collective point-to-point iterative learning controller designed through collective intelligence for independent point-to-point control tasks. The article also introduces switched reference and corresponding iterative learning control switching strategies, and verifies the effectiveness of the algorithm through a simulation example.