Particle-based Gaussian process optimization for input design in nonlinear dynamical models

TL;DR

This paper introduces a particle-based Gaussian process optimization method to efficiently design inputs for identifying nonlinear state space models by maximizing Fisher information, reducing computational costs.

Contribution

The paper presents a novel combination of particle methods and Gaussian process optimization for input design in nonlinear models, improving efficiency and accuracy.

Findings

Effective input design for nonlinear models demonstrated

Reduced computational cost via Gaussian process optimization

Numerical examples validate the approach

Abstract

We propose a novel approach to input design for identification of nonlinear state space models. The optimal input sequence is obtained by maximizing a scalar cost function of the Fisher information matrix. Since the Fisher information matrix is unavailable in closed form, it is estimated using particle methods. In addition, we make use of Gaussian process optimization to find the optimal input and to mitigate the problem of a large computational cost incurred by the particle filter, as the method reduces the number of functional evaluations. Numerical examples are provided to illustrate the performance of the resulting algorithm.