Learning to Control Two-Wheeled Self-Balancing Robot Using Reinforcement Learning Rules and Fuzzy Neural Networks

This paper present a novel method to control the balance of a two-wheeled robot by using reinforcement learning and fuzzy neural networks(FNN) which can guarantees the convergence and rapidity when the model of the robot is not available and the agent has no a prior knowledge. Furthermore it can effectively control the task of continuous states and actions. The simulation and experiment results demonstrate that it not only can learn to control the two-wheeled robot system in a short time, but also maintain the balance of two-wheeled robot when the parameters of two-wheeled change a lot.