Multilayer perceptron functional adaptive control for trajectory tracking of wheeled mobile robots

Abstract

Sigmoidal multilayer perceptron neural networks are proposed to effect functional adaptive control for handling the trajectory tracking problem in a nonholonomic wheeled mobile robot. The scheme is developed in

discrete time and the multilayer perceptron neural networks are used for the estimation of the robot’s nonlinear kinematic functions, which are assumed to be unknown. On-line weight tuning is achieved by employing the

extended Kalman filter algorithm based on a specifically formulated multiple-input, multiple-output, stochastic model for the trajectory error dynamics of the mobile base. The estimated functions are then used on a

certainty equivalence basis in the control law proposed in (Corradini et al., 2003) for trajectory tracking. The performance of the system is analyzed and compared by simulation.