Optimal Control of Oscillation Timing and Entrainment Using Large Magnitude Inputs: An Adaptive Phase-Amplitude-Coordinate-Based Approach

TL;DR

This paper develops an adaptive phase-amplitude control method for oscillatory systems, enabling effective entrainment with large inputs, and demonstrates its superiority over previous methods through data-driven model reduction and optimal control in complex systems.

Contribution

It introduces a novel adaptive phase-amplitude reduction framework that handles large inputs and improves control of oscillation timing and entrainment.

Findings

Adaptive control significantly outperforms previous methods.

Data-driven reduction accurately captures system dynamics.

Method applicable to large populations of coupled oscillators.

Abstract

Given the high dimensionality and underlying complexity of many oscillatory dynamical systems, phase reduction is often an imperative first step in control applications where oscillation timing and entrainment are of interest. Unfortunately, most phase reduction frameworks place restrictive limitations on the magnitude of allowable inputs, limiting the practical utility of the resulting phase reduced models in many situations. In this work, motivated by the search for control strategies to hasten recovery from jet-lag caused by rapid travel through multiple time zones, the efficacy of the recently developed adaptive phase-amplitude reduction is considered for manipulating oscillation timing in the presence of a large magnitude entraining stimulus. The adaptive phase-amplitude reduced equations allow for a numerically tractable optimal control formulation and the associated optimal stimuli significantly outperform those resulting from from previously proposed optimal control formulations. Additionally, a data-driven technique to identify the necessary terms of the adaptive phase-amplitude reduction is proposed and validated using a model describing the aggregate oscillations of a large population of coupled limit cycle oscillators. Such data-driven model reduction algorithms are essential in situations where the underlying model equations are either unreliable or unavailable.