Nonlinear Moving-Horizon Estimation Using State- and Control-Dependent Models

TL;DR

This paper introduces a novel nonlinear moving-horizon estimation algorithm that improves accuracy and computational efficiency by using state- and control-dependent models, avoiding Jacobian reliance, and providing theoretical guarantees.

Contribution

The proposed SCD-MHE algorithm offers a new structured optimization approach for nonlinear estimation with convergence analysis and superior performance over traditional filters.

Findings

SCD-MHE outperforms EKF and UKF in estimation accuracy.

The method reduces computational latency by over an order of magnitude.

Theoretical analysis guarantees convergence and bounded errors.

Abstract

This paper presents a state- and control-dependent moving-horizon estimation (SCD-MHE) algorithm for nonlinear discrete-time systems. Within this framework, a pseudo-linear representation of nonlinear dynamics is leveraged utilizing state- and control-dependent coefficients, where the solution to a moving-horizon estimation problem is iteratively refined. At each discrete time step, a quadratic program is executed over a sliding window of historical measurements. Moreover, system matrices are consecutively updated based upon prior iterates to capture nonlinear regimes. In contrast to the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), nonlinearities and bounds are accommodated within a structured optimization framework, thereby circumventing the reliance on local Jacobian matrices. Furthermore, theoretical analysis is presented to establish the convergence of the iterative sequence, and bounded estimation errors are mathematically guaranteed under uniform observability conditions. Finally, comparative numerical experiments utilizing a quadrotor vertical kinematics system demonstrate that the SCD-MHE achieves superior estimation accuracy relative to the EKF, the UKF, and a fully nonlinear moving-horizon estimator, while reducing per-step computational latency by over an order of magnitude.