A Probabilistic Distance-Based Stability Quantifier for Complex Dynamical Systems

Abstract

For a dynamical system, an attractor of the system may represent the `desirable' state. Perturbations acting on the system may push the system out of the basin of attraction of the desirable attractor. Hence, it is important to study the stability of such systems against reasonably large perturbations. We introduce a distance-based measure of stability, called `stability bound', to characterize the stability of dynamical systems against finite perturbations. This stability measure depends on the size and shape of the basin of attraction of the desirable attractor. A probabilistic sampling-based approach is used to estimate stability bound and quantify the associated estimation error. An important feature of stability bound is that it is numerically computable for any basin of attraction, including fractal basins. We demonstrate the merit of this stability measure using an ecological model of the Amazon rainforest, a ship capsize model, and a power grid model