SUPERVISED NEURAL NETWORK CONTROL OF REAL-TIME TWO WHEEL INVERTED PENDULUM

Abstract

This research paper investigates an intelligent control technique stabilizing a real-time model ofthe two-wheel inverted pendulum. The TWIP model is a highly non-linear, open-loop, and unstable system which makes control a challenge. Initially,a state-feedback controller that uses the dynamical system states and control signals to construct the precise control decision is used to stabilize the system. Later, Supervised Feed-Forward Neural Networks (SFFNN) based on back propagation Levenberg-Marquardt optimization algorithm are trained by using real-time measurements of system states and motors control signals from state-feedback controller stabilization. SFFNN control the two-wheel inverted pendulum better than a state-feedback controller.