Synthesis of Matsuoka-Based Neuron Oscillator Models in Locomotion Control of Robots

Abstract

In this paper we present a numerical study of the Matsuoka-based neuron oscillator model. The Matsuoka-based neuron oscillator model is one of the most popular CPG (central pattern generator) models in robot motion control. In this paper, numerical simulation is conducted to analyze the influence of the parameters on the output signals. A mass-spring-damper system is used as an example to analyze the entrainment properties of the neuron oscillator. The main engineering application methods of these CPG-inspired control methods are concluded. The motivation is to present a practical guide to researchers and engineers interested in the CPG-inspired control approaches.