Adaptive Neural Network Control of a Self-balancing Two-wheeled Scooter

Abstract

This paper presents an adaptive neural network control for a two-wheeled self-balancing scooter for pedagogical purposes. A mechatronic system structure driven by two DC motors is described, and its mathematical modeling incorporating the friction between the wheels and motion surface is derived. By decomposing the overall system into two subsystems: rotation and inverted pendulum, we design two adaptive radial-basis-function (RBF) neural network (DOF) controllers to achieve self- balancing and rotation control. Experimental results indicate that the proposed controllers are capable of providing appropriate control actions to steer the vehicle in desired manners.