Automated and Sound Synthesis of Lyapunov Functions with SMT Solvers
Daniele Ahmed, Andrea Peruffo, Alessandro Abate

TL;DR
This paper presents a sound and automated method using SMT solvers to synthesize Lyapunov functions for various dynamical systems, ensuring stability proofs with formal guarantees.
Contribution
It introduces an inductive framework combining SMT solvers and optimization tools for sound Lyapunov function synthesis across linear, non-linear, and parametric models.
Findings
Scales to 10-dimensional models within reasonable time
Provides formal soundness proofs for Lyapunov functions
Determines regions of validity for non-linear models
Abstract
In this paper we employ SMT solvers to soundly synthesise Lyapunov functions that assert the stability of a given dynamical model. The search for a Lyapunov function is framed as the satisfiability of a second-order logical formula, asking whether there exists a function satisfying a desired specification (stability) for all possible initial conditions of the model. We synthesise Lyapunov functions for linear, non-linear (polynomial), and for parametric models. For non-linear models, the algorithm also determines a region of validity for the Lyapunov function. We exploit an inductive framework to synthesise Lyapunov functions, starting from parametric templates. The inductive framework comprises two elements: a learner proposes a Lyapunov function, and a verifier checks its validity - its lack is expressed via a counterexample (a point over the state space), for further use by the…
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