A neural net-based feedforward control scheme for mobile robots

Abstract

A neural-net based control algorithm for the tracking control of a two-wheeled mobile robot is considered. The neural network was designed to generate feedforward control input to nullify the error between output and desired trajectory. The neural network controller was combined with local digital PD controllers for each wheel position control. The feedback-error learning methods is used as the learning mechanism such that the learning and control can be performed simultaneously. To evaluate the effectiveness of the proposed control scheme, a series of tracking simulations were performed for the dynamic model of the mobile robot. >