Stochastic Fuzzy Controller Based on OCPFA and Applied on Two-Wheeled Self-balanced Robot

Abstract

This paper constructs a stochastic fuzzy controller based on OCPFA learning system to realize self-balancing control of two-wheeled robot. The OCPFA learning system is in fact a Probabilistic Finite Automata (PFA) which based on Skinner Operant Conditioning (Skinner OC). Reorientation mechanism which take for posture balance of orientation function as goal-orientation is designed to make response to the output control variable of fuzzy stochastic controller; Learning mechanism is designed to update probability of output control variable by using the response information from the environment to achieve the anticipant probability vector which can minimize orientation function. The designed stochastic fuzzy controller can choose the optimal control variable by interacting with the dynamic environment. The simulation indicate that the stochastic fuzzy controller 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.