Parameter and State Estimation of Experimental Chaotic Systems Using Synchronization

TL;DR

This paper investigates synchronization-based techniques for extracting parameters and states from experimental chaotic systems, focusing on the Colpitts oscillator, and compares different optimization and coupling methods to improve accuracy and robustness.

Contribution

It introduces new synchronization methods, analyzes the impact of model imperfections, and compares optimization techniques for parameter and state estimation in chaotic systems.

Findings

Optimized time-dependent coupling shows a structured pattern correlated with phase space.

Constrained methods improve synchronization of long datasets with minimal impact.

Initial value methods are faster and more flexible for practical applications.

Abstract

We examine the use of synchronization as a mechanism for extracting parameter and state information from experimental systems. We focus on important aspects of this problem that have received little attention previously, and we explore them using experiments and simulations with the chaotic Colpitts oscillator as an example system. We explore the impact of model imperfection on the ability to extract valid information from an experimental system. We compare two optimization methods: an initial value method and a constrained method. Each of these involve coupling the model equations to the experimental data in order to regularize the chaotic motions on the synchronization manifold. We explore both time dependent and time independent coupling. We also examine both optimized and fixed (or manually adjusted) coupling. For the case of an optimized time dependent coupling function u(t) we find a robust structure which includes sharp peaks and intervals where it is zero. This structure shows a strong correlation with the location in phase space and appears to depend on noise, imperfections of the model, and the Lyapunov direction vectors. Comparison of this result with that obtained using simulated data may provide one measure of model imperfection. The constrained method with time dependent coupling appears to have benefits in synchronizing long datasets with minimal impact, while the initial value method with time independent coupling tends to be substantially faster, more flexible and easier to use. We also describe a new method of coupling which is useful for sparse experimental data sets. Our use of the Colpitts oscillator allows us to explore in detail the case of a system with one positive Lyapunov exponent. The methods we explored are easily extended to driven systems such as neurons with time dependent injected current.