Self-balance control of two-wheeled robot based on Skinner's operant conditioned reflex

Abstract

For the movement-balance control of a two-wheeled self-balancing robot,we adopt the autoregression neuralnetwork-learning-algorithm based on Skinner's operant conditioned reflex theory as the learning mechanism,and use the autoregression neural-network to approximate the critic function in the optimization of behavioral decision,so that a twowheeled robot can obtain self-learning skills like a human being or an animal through studying and training in a model-free external environment to realize the movement balance control.Two simulation experiments are separately performed in the states with and without disturbance,respectively.The comparison of the respective results shows that learning mechanism with Skinner's operant conditioned reflex has a faster control skill in self-balance and a high robustness.This exhibits great research significance in theory and practice.