Hybrid Control of Autonomous Systems with Mixed-Integer Quadratic Optimization
Abstract
This research proposal offers new solution methods for hybrid control of autonomous systems with mixed-integer quadratic optimization. In particular, it describes new approaches for deriving strong relaxations and scalable algorithms for their convex quadratic optimization formulations with indicator variables. The main idea is to decompose the convex quadratic objective function in these formulations into a sum of convex quadratics defined over low-rank matrices and to derive strong convex relaxations for them. Separation and duality approaches will be investigated to find the best decompositions into low-rank blocks. Low-order relaxations and special algorithms will be developed for scalable implementations. The resulting models and algorithms will be tested on hybrid control systems through extensive computational experiments.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 11, 2020
- Source ID
- N000142012076
Entities
People
- Alper Atamtürk
Organizations
- Office of Naval Research
- United States Navy
- University of California Regents