Abstract
This paper presents a novel observer-critic-based event-triggered decentralized optimal tracking control of modular robot manipulators (MRMs). Based on the dynamic model of the MRM subsystem utilizing joint torque feedback (JTF) technique, the performance index function is constructed by integrating the designed adaptive observer and the tracking error fusion function including position error and velocity error. On the basis of adaptive dynamic programming (ADP) algorithm and the event-triggered mechanism, the decentralized optimal tracking control problem is settled by solving the Hamiltonian-Jacobi-Bellman (HJB) equation via constructing the critic neural network (NN). The closed-loop MRM system is proved to be asymptotically stable by using the Lyapunov stability theorem. Finally, experimental results illustrate the effectiveness of the developed control method.
Sign-in/Register to access full text options 
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.
