Abstract
In this paper we propose two dual decomposition methods based on smoothing techniques, called here the proximal center method and the interior-point Lagrangian method, to solve distributively separable convex problems. We show that some relevant centralized control problems can be recast as a separable convex problem for which our dual methods can be applied. The new dual optimization methods are suitable for application to distributed control since they are highly parallelizable, each subsystem uses local information and the coordination between the local controllers is performed via the Lagrange multipliers corresponding to the coupled dynamics or constraints.
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.
