Adaptive intelligent steering control of ball-riding human transporter

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.