Trajectory Tracking Control for Omnidirectional Mobile Robots Using Direct Adaptive Neural Network Dynamic Surface Controller

Abstract

The paper presents an application of direct Adaptive Neural Network Dynamic Surface Control (DSCNN) algorithm to design a controller for a four-wheel omnidirectional holonomic robot tracking the expected trajectories. The stability of the system is proved based on Lyapunov standards. The proposed controller is simulated on kinetic dynamic model of a four-wheel omnidirectional holonomic robot in the laboratory. The simulation results show that DSCNN is given better control qualities than sliding mode control (SMC), backstepping control (BSP), and the accuracy of the proposed controller and open the ability to use this one in reality.