Adaptive Nonlinear Hierarchical Control Based on RBF Neural Networks for Line-Following and Obstacle-Avoiding Mobile Robots under Dynamic Model Uncertainties

Abstract

This paper proposes an adaptive nonlinear neural-network-based control law for a line-following and obstacle-avoidance robot under dynamic model uncertainties. First, the mathematical model of the line-following and obstacle-avoidance robot (LF&OAR) is unified with the conventional line-following model by employing LiDAR and camera sensors to construct a virtual line along the obstacle boundary and reconnect it with the main line, while the robot dynamics are represented in a general form. The proposed controller is developed based on a hierarchical control structure. At the kinematic level, the desired linear and angular velocities are generated using the Lyapunov approach to ensure smooth line-tracking performance, even when the reference path has large curvature. At the dynamic level, a controller is designed based on the general dynamic model of a differential-drive mobile robot using the super-twisting sliding mode control (STSMC) technique. The unknown nonlinear functions in the general dynamic model are approximated by radial basis function neural networks (RBFNNs), and an adaptive law is derived to update the network weights online. Simulation results and comparisons with a conventional super-twisting sliding mode controller demonstrate the effectiveness of the proposed method in terms of fast convergence and small tracking errors. Furthermore, experimental results confirm the feasibility of implementing the proposed control law on a real robotic platform.