Estimating the domain of attraction for non-polynomial systems via LMI optimizations

This paper proposes a strategy for estimating the DA (domain of attraction) for non-polynomial systems via LFs (Lyapunov functions). The idea consists of converting the non-polynomial optimization arising for a chosen LF in a polynomial one, which can be solved via LMI optimizations. This is achieved by constructing an uncertain polynomial linearly affected by parameters constrained in a polytope which allows us to take into account the worst-case remainders in truncated Taylor expansions. Moreover, a condition is provided for ensuring asymptotical convergence to the largest estimate achievable with the chosen LF, and another condition is provided for establishing whether such an estimate has been found. The proposed strategy can readily be exploited with variable LFs in order to search for optimal estimates. Lastly, it is worth to remark that no other method is available to estimate the DA for non-polynomial systems via LMIs.