Distribution Control of Stochastic Oscillators via Periodic Feedforward and Population-Level Feedback

Abstract

We address the problem of steering the phase distribution of oscillators all receiving the same control input to a given target distribution. In a large population limit, the distribution of oscillators can be described by a probability density. Thus, we formulate the problem as distribution control over a Fokker--Planck equation. In particular, we consider the case where oscillators are subject to stochastic noise, for which the theoretical understanding is still lacking. First, we characterize the asymptotic reachability of the phase distribution under periodic feedforward control via the Fourier coefficients of the target density and the phase sensitivity function of oscillators. This enables us to design a periodic input that makes the stationary distribution of oscillators closest to the target by solving a convex optimization problem. Next, we devise a distribution controller combining periodic and population-level feedback control, where the feedback component is designed to accelerate the convergence of the distribution of oscillators. We exhibit some convergence results for the proposed method, including a result that holds even under measurement errors in the phase distribution. The effectiveness of the proposed method is demonstrated by a numerical example.