Abstract
This paper proposes a new method to design a reinforcement learning-based integrated kinematic and dynamic tracking control scheme for a nonholonomic wheeled mobile robot. The scheme uses just only one neural network to design an online adaptive synchronous policy iteration algorithm implemented as an actor critic structure. Our tuning law for the single neural network not only learns online a tracking-HJB equation to approximate both the optimal cost and the optimal control law but also guarantees closed-loop stability in real-time. The convergence and stability of the overall system are proven by Lyapunov theory. The simulation results for wheeled mobile robot verify the effectiveness of the proposed controller.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
