Neural Network Control for the Linear Motion of a Spherical Mobile Robot

This paper discusses the stabilization and position tracking control of the linear motion of an underactuated spherical robot. Including the actuator dynamics, the complete dynamic model of the robot is deduced, which is a third order, two-variable nonlinear differential system that holds underactuation, strong coupling characteristics brought by the mechanism structure of the robot. Different from traditional treatments no linearization is applied, whereas a single-input multiple-output PID (SIMO_PID) controller is proposed with a neural network controller to compensate the actuator nonlinearity. A six-input single-output CMAC_GBF (Cerebellar Model Articulation Controller with General Basis Function) neural network is employed, while the Credit Assignment (CA) learning method is adopted to obtain faster convergence rate than the classical back-propagation (BP) learning method. The proposed controller can be generalizable to other single-input multiple-output system with good real-time capability. MATLAB simulations are used to validate the control effects.