Abstract

This paper investigates a decentralized guaranteed cost control method of modular and reconfigurable robots (MRRs) based on adaptive dynamic programming (ADP) algorithm. First, we formulate the dynamic model of systems and the interconnected couplings are described. Second, we design a decentralized guaranteed cost controller. By establishing a critic neural network, a learning-based adaptive dynamic programming algorithm is proposed to solve the Hamilton-Jacobi-Bellman (HJB) equation, and then the approximate optimal control policy can be derived. The asymptotic stability of the closed-loop system is proved based on the Lyapunov theory. Finally, simulations are performed for the 2-DOF MRRs to verify the effectiveness of the proposed method.

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 call

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.