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

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.