Abstract
In this paper, we propose a control system based on auto-fusion cerebellar perceptron improved model using feedback error learning which imitates the human cerebellum, and it is applied to the consensus problem of a multi-agent system (MAS). It is important to control multiple agents because each of them has own scale and complexity. Therefore, the coordinative control of multi-agent system for each autonomous decision making has been focused. To control MAS, we consider use of feedback error learning related to biological movement control, also, propose an auto fusion mechanism for mitigation of neuronal fluctuation. We call the proposed system an “Auto-Fusion Cerebellar Perceptron improved model-based Control System (AFCPCS)” here. Through the computer simulation for a consensus problem of MAS, we show the effectiveness of the proposed method.
