Stochastic pinning control of chaotic maps

What is it about?

Emerging collective dynamics, such as network synchronization, are observed in a broad spectrum of phenomena. Often, the network needs to synchronize onto a desired trajectory, as in animal grouping, where the leaders guide their conspecifics to set locations. Pinning control was introduced to address this problem, but in many scenarios control actions cannot be persistently applied. To tackle this relevant issue, we study a class of stochastic pinning strategies for networks of chaotic maps.

Featured Image

Why is it important?

In pinning control, an external node generates a reference trajectory and exerts a control action on a small fraction of the network nodes. Typically, control actions cannot be persistently applied, as for animal grouping in which leadership is spatially and temporally distributed. Here, we overcome this limitation by introducing a class of pinning strategies stochastically implemented on a network of chaotic maps and we establish a toolbox of analytical tools to investigate their effectiveness.