Neuro-adaptive dynamic control for trajectory tracking of mobile robots

Abstract

This paper presents a novel functional-adaptive dynamic controller for trajectory tracking of nonholonomic wheeled mobile robots. The controller is developed in discrete-time and employs a Gaussian radial basis function neural network for the estimation of the robot’s nonlinear dynamic functions, which are assumed to be completely unknown. Optimal on-line weight tuning is achieved by employing the Kalman filter algorithm, based on a specifically formulated stochastic inverse dynamic identification model of the mobile base. A discrete-time dynamic control law employing the estimated functions is proposed and cascaded with a trajectory tracking kinematic controller. The performance of the complete system is analysed and compared by realistic simulations.