Adaptive Neural Control for Mobile Robots Autonomous Navigation

TL;DR

This paper introduces an adaptive neural control method for mobile robots that combines kinematic steering and velocity dynamics learning, enabling effective tracking under varying conditions without assuming perfect velocity tracking.

Contribution

It proposes a novel combined control strategy using neural networks to learn inverse dynamics and stabilize the robot without requiring perfect velocity tracking.

Findings

Successful velocity trajectory tracking demonstrated

Neural network controller adapts to system disturbances

Improved robustness over traditional control methods

Abstract

This paper presents a combined strategy for tracking a non-holonomic mobile robot which works under certain operating conditions for system parameters and disturbances. The strategy includes kinematic steering and velocity dynamics learning of mobile robot system simultaneously. In the learning controller (neural network based controller) the velocity dynamics learning control takes part in tracking of the reference velocity trajectory by learning the inverse function of robot dynamics while the reference velocity control input plays a role in stabilizing the kinematic steering system to the desired reference model of kinematic system even without using the assumption of perfect velocity tracking.