Neuro-adaptive hierarchical sliding mode control for a 3-wheeled mobile robot with disturbance rejection

Abstract

High tracking accuracy and fast convergence are essential features in the control of wheeled mobile robots for autonomous navigation applications. However, the design of a control system for these robots faces significant challenges due to the inherent complexity of their nonlinear dynamics and their non-holonomic underactuated nature. This article introduces a novel control framework for trajectory tracking of a three-wheeled mobile robot (3WMR), considering external and internal disturbances. To address the uncertain nonlinear and underactuated non-holonomic dynamics of the 3WMR, an adaptive hierarchical fast terminal sliding mode control (AHFTSMC) strategy is proposed. In this approach, an adaptive neural network scheme adjusts the sliding surface coefficients in real time to minimize tracking errors and mitigate the chattering phenomenon. In addition, a finite time disturbance observer (FTDO) is designed to accurately estimate and compensate for unknown lumped disturbances, which helps to improve the disturbance rejection capability. The stability of the closed-loop system is demonstrated using Lyapunov theory. The proposed control approach is validated by numerical simulations, and a comparative analysis of recent hierarchical sliding mode controllers (SMC) is performed. The results demonstrate that the proposed approach achieves superior performance in terms of fast convergence and tracking accuracy, which are crucial features for the autonomous navigation of mobile robots.