Neural network control for the linear motion of a spherical mobile robot

This paper discussed the stabilization and position tracking control of the linear motion of an underactuated spherical robot. By considering the actuator dynamics, a complete dynamic model of the robot is deduced, which is a complex third order, two variables nonlinear differential system and those two variables have strong coupling due to the mechanical structure of the robot. Different from traditional treatments, no linearization is applied to this system but a single-input multiple-output PID (SIMO-PID) controller is designed by adopting a six-input singleoutput CMAC-GBF (Cerebellar Model Articulation Controller with General Basis Function) neural network to compensate the actuator nonlinearity and the credit assignment (CA) learning method to obtain faster convergence of CMAC-GBF. The proposed controller is generalizable to other single-input multiple-output system with good real-time capability. Simulations in Matlab are used to validate the control effects.