Abstract
In this paper, an event-triggered optimal adaptive control is developed for robot trajectory tracking system. Due to the nonlinearity of Hamilton function, we apply the actor-critic neural network structure to solve it. Firstly, the critic network is used to estimate the cost function and the actor network is used to estimate the optimal event-triggered control law. Due to the advantage of event-triggered method, the weight update rate of actor-critic neural network only occurs when the triggering condition is violated, which save a lot of communication resources. Then, the event-triggered robot trajectory tracking system is ultimately bounded by Lyapunov stability analysis. Finally, the simulation show that the proposed method is effective.
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.