A novel dual adaptive neuro-controller based on the unscented transform for mobile robots

Abstract

This paper proposes a novel dual adaptive neuro-control scheme based on the unscented transform for the dynamic control of nonholonomic wheeled mobile robots. The controller is developed in discrete time and the robot nonlinear dynamic functions are unknown to the controller. A multilayer perceptron neural network is used to approximate the nonlinear robot dynamics. The network is trained online via a specifically devised unscented Kalman predictor. In contrast to the majority of adaptive control techniques hitherto proposed in the literature, the controller presented in this paper does not rely on the heuristic certainty equivalence assumption, but accounts for the estimates’ uncertainty via the principle of dual adaptive control. Moreover, the novel dual adaptive control law employs the unscented transform to improve on the first-order Taylor approximations inherent in previously published dual adaptive schemes. Realistic simulations, including comparative Monte Carlo tests, are used to illustrate the effectiveness of the proposed approach.