Mobile Robots Adaptive Control Using Neural Networks

TL;DR

This paper introduces a neural network-based feed-forward control method for mobile robots that effectively manages non-linear dynamics and uncertainties, enhancing control accuracy and adaptability.

Contribution

It presents a novel neural network control approach that incorporates the full non-linear model and uncertainties of mobile robots, improving control robustness.

Findings

Neural network control compensates for model uncertainties.

Enhanced control accuracy demonstrated in simulations.

Framework adaptable to various mobile robot models.

Abstract

The paper proposes a feed-forward control strategy for mobile robot control that accounts for a non-linear model of the vehicle with interaction between inputs and outputs. It is possible to include specific model uncertainties in the dynamic model of the mobile robot in order to see how the control problem should be addressed taking into consideration the complete dynamic mobile robot model. By means of a neural network feed-forward controller a real non-linear mathematical model of the vehicle can be taken into consideration. The classical velocity control strategy can be extended using artificial neural networks in order to compensate for the modelling uncertainties. It is possible to develop an intelligent strategy for mobile robot control.