Adaptive Nonsingular Terminal Sliding-Mode Formation Control Using ORFWNN for Uncertain Networked Heterogeneous Mecanum-Wheeled Omnidirectional Robots

摘要

This paper presents an adaptive nonsingular terminal sliding mode formation control by means of output recurrent fuzzy wavelet neural networks for a group of networked heterogeneous Mecanum-wheeled omnidirectional robots with uncertainties. The dynamic behavior of each uncertain heterogeneous omnidirectional robot is modelled by a reduced three-input-three-output second-order state equation with uncertainties and the multi-robot system is modeled by graph theory. Through the Lyapunov stability theory and online learning of the system uncertainties via output recurrent fuzzy wavelet neural networks, an adaptive nonsingular terminal sliding mode control approach is offered to attain finite-time formation control in presence of uncertainties. Conducting three simulations are to show the effectiveness and merit of the proposed method.