Neural Network-Based Adaptive Tracking Control for a Nonholonomic Wheeled Mobile Robot Subject to Unknown Wheel Slips

Abstract

In this paper, Lagrange formula is employed with the purpose of modelling both the kinematics and dynamics of a nonholonomic wheeled mobile robot (WMR) subject to unknown wheel slips, model uncertainties such as unstructured unmodelled dynamic components, and unknown external disturbances such as unknown external forces. Afterwards, an adaptive tracking controller based on a radial basis function neural network (RBFNN) with an online weight tuning algorithm is proposed for tracking a predefined trajectory. Prior neural network offline training is not needed for the weights since they are easily initialized. Thanks to this proposed control approach, a desired tracking performance is obtained in which position tracking errors uniformly ultimately converge to an arbitrarily small neighborhood of the origin. In the sense of Lyapunov and LaSalle extension, the stability of the whole closed-loop system is guaranteed to achieve this desired tracking performance. The comparative results of computer simulation have validated the rightness and efficiency of the proposed controller.

Tracking Control of Wheeled Mobile Robot Based on MMPC

“…Literature [4] designed a synovial film controller based on the WMR kinematics model, even if the WMR has a large initial error in the control, it can get better control. Literature [5] considered WMR parameters and non-parameters two kinds of uncertainty, combined with adaptive control and neural network methods, to solve the mobile robot center of gravity offset, model uncertainty and other factors. Literature [6] studied the trajectory tracking control of WMR on uneven ground, and designed a double closed-loop strategy; the external disturbance was estimated by the inner loop, and the expected speed was output by the outer loop, which realized the smooth turning of WMR.…”