DESIGNING AN ADAPTIVE CONTROLLER FOR TWO-WHEELED SELF-BALANCING MOBILE ROBOT USING HIERARCHICAL SLIDING CONTROL STRATEGY AND RADIAL BASIS FUNCTION NEURAL NETWORK

Abstract

This paper presents a novel adaptive controller for two-wheeled selfbalancing mobile robots combining sliding mode control and hierarchical sliding
control techniques. In addition, the radial basis function neural networks
(RBFNN) are also applied to approximate the uncertain components in the
system. The stability of the closed-loop control system is proven based on the
Lyapunov principle. The simulation results show that the proposed controller's
response quality is excellent even if the system is affected by unexpected
external disturbances.