Adaptive intelligent steering control of ball-riding human transporter

Abstract

This paper presents an adaptive intelligent steering controller using backstepping sliding-mode control and recurrent wavelet fuzzy cerebellar model articulation controller (RWFCMAC) and for an uncertain ball-riding human transporter in presence of significant system uncertainties. The proposed controller operates at two independent modes: self-balancing and station keeping. The self-balancing mode is used to balance by following the rider's two-dimensional inclination angles, while the station-keeping mode is aimed to permit the rider to keep the vehicle at a target position. The RWFCMAC is designed to online learn the uncertainties caused by riders' weights and different parameters. The superior performance and merit of the proposed control methods are well exemplified by conducting two simulations.