Dual adaptive neurocontrol of mobile robots using the unscented transform : Monte Carlo and experimental validation

Abstract

In contrast to most adaptive schemes, dual adaptive controllers do not rely on the heuristic certainty equivalence assumption, but aim to strike a balance between estimation and control at all times. Yet, few such controllers have ever been implemented and tested in practice, especially within the context of intelligent control, and to the best of our knowledge none on mobile robots. With the help of Mont Carlo simulation and real-life experiments, this article presents and validates a novel dual adaptive neurocontroller based on the unscented transform, for the dynamic control of nonholonomic wheeled mobile robots. The robot nonlinear dynamic functions are unknown to the controller and a multilayer perceptron neural network, trained via an unscented Kalman predictor, is used for their approximation in real-time. Moreover, the proposed novel dual adaptive control law employs the unscented transform to improve further the system’s performance.