基于Skinner操作条件反射的两轮机器人自平衡控制

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

中文关键词  Skinner操作条件反射  自回归神经网络  两轮机器人  自平衡控制  鲁棒性

英文关键词  Skinner’s operant conditioned reflex  autoregression neural-network  two-wheeled robot  self-balance control  robustness

中文摘要

      针对两轮自平衡机器人的运动平衡控制问题, 采用了基于Skinner操作条件反射理论的自回归神经网络学习算法作为机器人的学习机制, 利用自回归神经网络对评价函数进行逼近, 以实现对行为决策的优化, 从而使机器人能够在无需外部环境模型的情况下, 通过学习和训练, 获得像人或动物一样的自主学习技能, 解决了两轮机器人的运动平衡控制问题. 最后分别在无扰动和有扰动的两种状态下设计了仿真实验并进行了比较. 结果表明, 该操作条件反射学习机制具有较快的自主平衡控制技能和较好的鲁棒性能, 体现了较高的理论研究意义和工程应用价值.

英文摘要

      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.