Multilayer perceptron dual adaptive control for mobile robots

Abstract

This paper presents a novel dual adaptive dynamic controller for trajectory tracking of nonholonomic wheeled mobile robots. The controller is developed in discrete-time and the robot's nonlinear dynamic functions are assumed to be unknown. A sigmoidal multilayer perceptron neural network is employed for function approximation, and its weights are estimated stochastically in real-time. In contrast to adaptive certainty equivalence controllers hitherto published for mobile robots, the proposed control law takes into consideration the estimates' uncertainty, thereby leading to improved tracking performance. The proposed method is verified by realistic simulations and Monte Carlo analysis.