Online Joint State Inference and Learning of Partially Unknown State-Space Models

TL;DR

This paper introduces a computationally efficient online method for joint state inference and learning of partially unknown state-space models, combining physical models with data-driven basis functions, suitable for real-time large-scale applications.

Contribution

It presents a reduced-complexity approach using compactly supported radial basis functions and an approximate Kalman gain for online joint state and model learning.

Findings

Competitive performance in system dynamics estimation.

Real-time applicability to large-scale problems.

Significant reduction in computational complexity.

Abstract

A computationally efficient method for online joint state inference and dynamical model learning is presented. The dynamical model combines an a priori known, physically derived, state-space model with a radial basis function expansion representing unknown system dynamics and inherits properties from both physical and data-driven modeling. The method uses an extended Kalman filter approach to jointly estimate the state of the system and learn the unknown system dynamics, via the parameters of the basis function expansion. The key contribution is a computational complexity reduction compared to a similar approach with globally supported basis functions. By using compactly supported radial basis functions and an approximate Kalman gain, the computational complexity is considerably reduced and is essentially determined by the support of the basis functions. The approximation works well when the system dynamics exhibit limited correlation between points well separated in the state-space domain. The method is exemplified via two intelligent vehicle applications where it is shown to: (i) have competitive system dynamics estimation performance compared to the globally supported basis function method, and (ii) be real-time applicable to problems with a large-scale state-space.