Neural Network Based Terminal Sliding Mode Control for WMRs Affected by an Augmented Ground Friction With Slippage Effect

Wheeled mobile robots (WMRs) encounter unavoidable slippage especially on the low adhesion terrain such that the robots stability and accuracy are reduced greatly. To overcome this drawback, this article presents a neural network (NN) based terminal sliding mode control (TSMC) for WMRs where an augmented ground friction model is reported by which the uncertain friction can be estimated and compensated according to the required performance. In contrast to the existing friction models, the developed augmented ground friction model corresponds to actual fact because not only the effects associated with the mobile platform velocity but also the slippage related to the wheel slip rate are concerned simultaneously. Besides, the presented control approach can combine the merits of both TSMC and radial basis function (RBF) neural networks techniques, thereby providing numerous excellent performances for the closed-loop system, such as finite time convergence and faster friction estimation property. Simulation results validate the proposed friction model and robustness of controller; these research results will improve the autonomy and intelligence of WMRs, particularly when the mobile platform suffers from the sophisticated unstructured environment.