模糊操作条件概率自动机仿生自主学习系统和机器人自平衡控制

Fuzzy operant conditioning probabilistic automaton bionic autonomous learning system and robot self-balancing control

为了实现两轮机器人的自平衡控制,利用Skinner操作条件反射机理,以概率自动机为平台,融入模糊推理,构造了模糊操作条件概率自动机(OCPA)仿生自主学习系统.该学习系统是一个从状态集合到操作行为集合的随机映射,采用操作条件反射学习机制,从操作行为集合中随机学习作为控制系统控制信号的最优行为,并利用学习到的操作行为取向值信息,调整操作条件反射学习算法.此外,学习系统还引入行为熵,以验证其自学习和自组织能力.应用于两轮机器人自平衡控制的仿真结果,验证了模糊OCPA学习系统的可行性.

A fuzzy operant conditioning probabilistic automaton（OCPA） bionic autonomous learning system is constructed based on Skinner operant conditioning theory and combined with the probabilistic automaton and fuzzy inference for realizing a two-wheeled robot self-balancing control. The learning system is a stochastic mapping from state sets to operant action sets. The optimal action for controlling the system is stochastically learned from the operant action set by adopting operant conditioning learning algorithm; in the same time the orientation value information of the learned operant action is used to adjust the operant conditioning learning algorithm. In addition, the action entropy is added to verify the self-learning and self-organizing ability of the learning system. In the simulation, a two-wheeled robot self-balancing control is realized, demonstrating the feasibility of the fuzzy OCPA learning system.