A guided residual search for nonlinear state-space identification

TL;DR

This paper introduces a guided residual search method for nonlinear state-space model identification, decomposing complex optimization into manageable steps to enhance reliability and efficiency, with promising results on benchmark datasets.

Contribution

It presents a novel decomposition approach combining residual estimation and multiple-shooting optimization for nonlinear state-space identification.

Findings

Competitive performance on benchmark datasets

Improved convergence over naive initialization

Effective decomposition of complex optimization problems

Abstract

Parameter estimation of nonlinear state-space models from input-output data typically requires solving a highly non-convex optimization problem prone to slow convergence and suboptimal solutions. This work improves the reliability and efficiency of the estimation process by decomposing the overall optimization problem into a sequence of tractable subproblems. Based on an initial linear model, nonlinear residual dynamics are first estimated via a guided residual search and subsequently refined using multiple-shooting optimization. Experimental results on two benchmarks demonstrate competitive performance relative to state-of-the-art black-box methods and improved convergence compared to naive initialization.