Self-dual approximations to fully convex impulsive systems
Document Type
Conference Proceeding
Publication Date
12-27-2016
Abstract
Fully convex optimal control problems contain a Lagrangian that is jointly convex in the state and velocity variables. Problems of this kind have been widely investigated by Rockafellar and collaborators if the Lagrangian is coercive and without state constraints. A lack of coercivity implies the dual has nontrivial state constraints, and vice versa (that is, they are dual concepts in convex analysis). We consider a framework using Goebel's self-dualizing technique that approximates both the primal and dual problem simultaneously and maintains the duality relationship. Previous results are applicable to the approximations, and we investigate the limiting behavior as the approximations approach the original problem. A specific example is worked out in detail.
Publication Source (Journal or Book title)
2016 IEEE 55th Conference on Decision and Control Cdc 2016
First Page
6198
Last Page
6203
Recommended Citation
Hermosilla, C., & Wolenski, P. (2016). Self-dual approximations to fully convex impulsive systems. 2016 IEEE 55th Conference on Decision and Control Cdc 2016, 6198-6203. https://doi.org/10.1109/CDC.2016.7799222