Adaptive-Neural-Network-Based Trajectory Tracking Control for a Nonholonomic Wheeled Mobile Robot With Velocity Constraints

Abstract

In this article, an adaptive neural network control scheme is presented for an uncertain wheeled mobile robot (WMR) with velocity constraints and nonholonomic constraints. In practice, dynamic parameters of the system, which may change in some conditions, are hard to obtain precisely, and the velocity of the WMR should be constrained for safety. To deal with the uncertainty of the robot, adaptive neural networks are used to approximate unknown robotic dynamics, and the barrier Lyapunov function is used to guarantee the constraint on velocity. The tracking error of the closed-loop system is proven to converge to a small neighborhood of zero. Both simulation studies and practical experiments are provided to illustrate the effectiveness of the proposed control scheme.