中文摘要：

针对模糊规则的自动生成问题，采用Skinner操作条件反射（OC）和概率有限自动机（PFA）构成的OCPFA学习系统，设计能对模糊规则进行自学习和自组织的随机模糊控制策略。本策略首先采用设计的OC学习机制，从模糊行为集合中随机选取一个模糊行为，作为模糊规则的后件；然后利用环境对选取模糊行为的反馈信息，更新OC学习机制；最后依据更新后的OC学习机制，重新选取模糊后件行为，直至学习到最优的模糊规则。理论证明，其自学习和自组织过程在概率意义上是收敛的。在两轮自平衡机器人上的仿真和实验均表明，设计的随机模糊控制策略不需要系统的模型，成功地实现了机器人的自平衡控制，并提高了机器人的学习速度和控制精度。

英文摘要：

According to the problem of fuzzy rule automatic generation, a self-organization stochastic fuzzy control strategy is designed by adopting OCPFA learning system which is constructed by Skinner operant conditioning（OC） and probabilistic finite automata（PFA）. The designed OC learning mechanism is fristly adopted in the strategy to stochasticly select a fuzzy action which is used as the consequent part of fuzzy rule from the fuzzy action sets; then the OC learning mechanism is updated by using feedback information of selected fuzzy action which comes from environment; finally a new fuzzy consequent action is selected based on the updated 0C learning mechanism. The process of self-learning and self-organising are theoretically proved convergent in sense of probability. Both the simulation and experiment indicate that the stochastic fuzzy control strategy can he successfully applied in self-balancing control of twowheeled robot and the learning rate and control precision of robot are improved.