Data-Driven Stabilization of Periodic Orbits

Abstract

Periodic orbits are among the simplest non-equilibrium solutions to dynamical systems, and they play a significant role in our modern understanding of the rich structures observed in many systems. For example, it is known that embedded within any chaotic attractor are infinitely many unstable periodic orbits (UPOs) and so a chaotic trajectory can be thought of as 'jumping' from one UPO to another in a seemingly unpredictable manner. A number of studies have sought to exploit the existence of these UPOs to control a chaotic system. These methods rely on introducing small, precise parameter manipulations each time the trajectory crosses a transverse section to the flow. Typically these methods suffer from the fact that they require a precise description of the Poincaré mapping for the flow, which is a difficult task since there is no systematic way of producing such a mapping associated to a given system. Here we employ recent model discovery methods for producing accurate and parsimonious parameterdependent Poincaré mappings to stabilize UPOs in nonlinear dynamical systems. Specifically, we use the sparse identification of nonlinear dynamics (SINDy) method to frame model discovery as a sparse regression problem, which can be implemented in a computationally efficient manner. This approach provides an explicit Poincaré mapping that faithfully describes the dynamics of the flow in the Poincaré section and can be used to identify UPOs. For each UPO, we then determine the parameter manipulations that stabilize this orbit. The utility of these methods are demonstrated on a variety of differential equations, including the Rössler system in a chaotic parameter regime.

Symmetry reduction for deep reinforcement learning active control of chaotic spatiotemporal dynamics

“…We speculate that this "strategy" of finding and stabilizing an unstable recurrent solution (steady state, periodic orbit) might arise in RL control of a wide variety of systems displaying complex dynamics. We contrast this with other recent data-driven control-target identifying methods, such as [2] which identifies and stabilizes periodic orbits by approximating their Poincaré mapping, whereas here we seek targets defined by macroscopic properties, and the learned solution turns out to be a recurrent solution.…”