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Self-organization stochastic fuzzy control based on OCPFA and applied on self-balanced robot

This paper constructs a stochastic fuzzy controller to realize self-balancing control of two-wheeled robot. The rule of fuzzy controller is stochastic, which is automatically generated by an OCPFA learning system and optimized online. The OCPFA learning system is in fact a Probabilistic Finite Automata (PFA) which based on Skinner Operant Conditioning (Skinner OC), and it is composed by a bionic reorientation mechanism and an OC learning mechanism. The reorientation mechanism take for orientation function of robot posture balance as the goal-orientation, and response to the output control variable of fuzzy stochastic controller; Learning mechanism updated probability contribution of output control variable by using the response information form the environment and then choose the optimal control variable according to the new probability contribution. The OCPFA learning system gradually adapts to the environment changes by interacting with the dynamic environment based on which the learning system realize the self-organization of stochastic fuzzy control rules and show character of autonomous learning. In the same time the theoretical prove that the process of autonomous learning is convergent in sense of probability. The simulation indicate the stochastic fuzzy controller can successfully applied in two-wheeled robot self-balancing without requiring the model of the robot and the robot can show control behavior of autonomous learning which similar to animal's OC learning behavior.