A numerical algebraic geometry approach to regional stability analysis of polynomial systems

We explore region of attraction (ROA) estimation for polynomial systems via the numerical solution of polynomial equations. Computing an optimal, stable sub-level set of a Lyapunov function is first posed as a polynomial optimization problem. Solutions to this optimization problem are found by solvi...

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Bibliographic Details
Main Authors: Wampler, Charles (Author), Permenter, Frank Noble (Contributor), Tedrake, Russell Louis (Contributor)
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE), 2014-10-14T14:02:46Z.
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Online Access:Get fulltext
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100 1 0 |a Wampler, Charles  |e author 
100 1 0 |a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory  |e contributor 
100 1 0 |a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science  |e contributor 
100 1 0 |a Permenter, Frank Noble  |e contributor 
100 1 0 |a Tedrake, Russell Louis  |e contributor 
700 1 0 |a Permenter, Frank Noble  |e author 
700 1 0 |a Tedrake, Russell Louis  |e author 
245 0 0 |a A numerical algebraic geometry approach to regional stability analysis of polynomial systems 
260 |b Institute of Electrical and Electronics Engineers (IEEE),   |c 2014-10-14T14:02:46Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/90911 
520 |a We explore region of attraction (ROA) estimation for polynomial systems via the numerical solution of polynomial equations. Computing an optimal, stable sub-level set of a Lyapunov function is first posed as a polynomial optimization problem. Solutions to this optimization problem are found by solving a polynomial system of equations using techniques from numerical algebraic geometry. This system describes KKT points and singular points not satisfying a regularity condition. Though this system has exponentially many solutions, the proposed method trivially parallelizes and is practical for problems of moderate dimension and degree. In suitably generic settings, the method can solve the underlying optimization problem to arbitrary precision, which could make it a useful tool for studying popular semidefinite programming based relaxations used in ROA analysis. 
546 |a en_US 
655 7 |a Article 
773 |t Proceedings of the 2013 American Control Conference