Distributed Control over Networks Using Smoothing Techniques

Authors:
Ion Necoara
Affiliations:
Automatic Control and Systems Engineering Department, Politehnica University of Bucharest, Bucharest, Romania 060042
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Year:
2009

Citing 4
Cited 0

Parallel and distributed computation: numerical methods

Parallel and distributed computation: numerical methods
Distributed Algorithms for Cooperative Control

IEEE Pervasive Computing
Cooperative Control of Distributed Multi-Agent Systems

Cooperative Control of Distributed Multi-Agent Systems
Distributed receding horizon control for multi-vehicle formation stabilization

Automatica (Journal of IFAC)

Quantified Score

Hi-index	0.00

Visualization

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.