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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.