Adaptive unscented Kalman filter-based online slip ratio control of wheeled-mobile robot

Abstract

This paper presents an adaptive unscented Kalman filter (AUKF)-based sliding mode control (SMC) method for effective tracking of the slip ratio applicable to wheeled mobile robots (WMR). The adaptive unscented Kalman filter is developed to estimate the vehicle longitudinal velocity and the wheel angular velocity in the presence of system parameter variation and disturbances. An adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the unscented Kalman filter context. Furthermore, a sliding mode controller is designed for slip tracking control of the uncertain nonlinear dynamical system in the presence of model uncertainties, parameter fluctuations, and disturbances. The effectiveness of the controller has been verified by carrying out simulation studies. The controller is able to provide accurate reference slip tracking of the mobile robot, despite uncertainties present in the robot/wheel dynamics and changing terrain conditions. It is also demonstrated that the adaptive concept of AUKF leads to better results than the unscented Kalman filter in estimating the vehicle velocity which is difficult to measure in actual practice.