Journal of Theoretical
and Applied Mechanics
56, 4, pp. 1193-1204, Warsaw 2018
DOI: 10.15632/jtam-pl.56.4.1193

Robust neural networks control of omni-mecanum wheeled robot with hamilton-jacobi inequality

Zenon Hendzel
This paper presents a novel approach to the problem of controlling mechanical objects of
unspecified description, considering variable operating conditions. The controlled object is a
mobile robot with mecanum wheels (MRK M). To solve the control task, taking into account
compensation for nonlinearity and the object variable operating conditions, the Lyapunov
stability theory is applied, including the Hamilton-Jacobi (HJ) inequality. A neural network
with basic sigmoid functions is used to compensate for the nonlinearity and variable operating
conditions of the robot. A simulation example is provided in order to evaluate the
analytical considerations. The simulation results obtained confirmed high accuracy of the
predicted robot motion in variable operating conditions.
Keywords: mechatronics, mobile robot, mecanum wheels, Hamilton-Jacobi inequality

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