Intelligent Adaptive Motion Control Using Fuzzy Basis Function Networks for Electric Unicycle

This paper presents two intelligent adaptive controllers, called self-balancing and speed controllers, for self-balancing and motion control, respectively, of an electric unicycle using fuzzy basis function networks (FBFN), which are employed to approximate model uncertainties and unknown friction between the wheel and the terrain surface. Both controllers are established based on the linearized model of the vehicle whose model uncertainties and parameter variations are caused by different riders and terrain. An adaptive backstepping controller together with online learning FBFN and sensing information of the rider's body inclination then is presented to achieve self-balancing motion control. By adding an electronic throttle as the input device of speed commands, a decoupling sliding-mode controller with online learning FBFN is proposed to accomplish self-balancing and speed control. The performance and merit of the two proposed control methods are exemplified by conducting four simulations and three experiments on a laboratory-built electric unicycle.