Automated sampling-based stability verification and DOA estimation for nonlinear systems

R.V. Bobiti, M. Lazar

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

This paper develops a new sampling-based method for stability verification of piecewise continuous nonlinear systems via Lyapunov functions. Depending on the nonlinear system dynamics, the candidate Lyapunov function and the set of states of interest, verifying stability requires solving complex, possibly non-convex or infeasible optimization problems. To avoid such problems, the proposed approach firstly distributes the verification of Lyapunov's inequality on a finite sampling of a bounded set of states of interest. Secondly, it extends the validity of Lyapunov's inequality to an infinite, bounded set of states by automatically exploiting local continuity properties. A sampling-based method for estimating the domain of attraction (DOA) via level sets of a validated Lyapunov function is also presented. Efficient state-space exploration is achieved using multi-resolution sampling and hyper-rectangles as basic sampling blocks. The operations that need to be performed for each sampling point in the state-space can be carried out in parallel, which improves scalability. The proposed methodology is illustrated for various benchmark examples from the literature.

Original languageEnglish
Pages (from-to)3659-3674
JournalIEEE Transactions on Automatic Control
Volume63
Issue number11
DOIs
Publication statusPublished - 1 Nov 2018

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Nonlinear systems
Sampling
Lyapunov functions
Scalability

Keywords

  • Direction-of-arrival estimation
  • Estimation
  • Level set
  • Lyapunov methods
  • Nonlinear systems
  • Optimization
  • Stability analysis

Cite this

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Automated sampling-based stability verification and DOA estimation for nonlinear systems. / Bobiti, R.V.; Lazar, M.

In: IEEE Transactions on Automatic Control, Vol. 63, No. 11, 01.11.2018, p. 3659-3674.

Research output: Contribution to journalArticleAcademicpeer-review

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AB - This paper develops a new sampling-based method for stability verification of piecewise continuous nonlinear systems via Lyapunov functions. Depending on the nonlinear system dynamics, the candidate Lyapunov function and the set of states of interest, verifying stability requires solving complex, possibly non-convex or infeasible optimization problems. To avoid such problems, the proposed approach firstly distributes the verification of Lyapunov's inequality on a finite sampling of a bounded set of states of interest. Secondly, it extends the validity of Lyapunov's inequality to an infinite, bounded set of states by automatically exploiting local continuity properties. A sampling-based method for estimating the domain of attraction (DOA) via level sets of a validated Lyapunov function is also presented. Efficient state-space exploration is achieved using multi-resolution sampling and hyper-rectangles as basic sampling blocks. The operations that need to be performed for each sampling point in the state-space can be carried out in parallel, which improves scalability. The proposed methodology is illustrated for various benchmark examples from the literature.

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