A Control System Based on an Improved Model Auto‐Fusion Cerebellar Perceptron and Its Application to the Consensus Problem

Abstract

SUMMARYIn this paper, we propose a control system based on an improved model auto‐fusion cerebellar perceptron using feedback error learning (FEL) which imitates the human cerebellum, and apply it to the consensus problem of a multiagent system (MAS). It is important to control multiple agents because each of them has its own scale and complexity. Therefore, coordinative control of the MAS for each autonomous decision‐making instance has been taken as the focus. To control MAS, we consider use of FEL related to biological movement control. We also propose an auto‐fusion mechanism for mitigation of neuronal fluctuation. We call the proposed system the “Auto‐Fusion Cerebellar Perceptron Improved Model‐Based Control System (AFCPCS)” here. Through a computer simulation of the MAS consensus problem, we demonstrate the effectiveness of the proposed method.