Strong nonlinear detectability and moving horizon estimation for nonlinear systems with unknown inputs

TL;DR

This paper introduces the concept of strong nonlinear detectability and develops moving horizon estimators for nonlinear systems with unknown inputs, ensuring bounded estimation errors even with unbounded disturbances.

Contribution

It proposes a new detectability condition for nonlinear systems and designs two types of moving horizon estimators based on this concept, improving estimation robustness and computational efficiency.

Findings

Both MHE-based UISE variants outperform conventional MHE in simulations.

The two-stage MHE-based UISE is more computationally efficient.

The proposed methods effectively handle unbounded unknown inputs.

Abstract

This paper considers state estimation for general nonlinear discrete-time systems subject to measurement noise and possibly unbounded unknown inputs. To approach this problem, we first propose the concept of strong nonlinear detectability. This condition is sufficient and necessary for the existence of unknown input state estimators (UISEs), which reconstruct states from noisy sampled measurements and yield bounded estimation error even for unbounded unknown inputs. Based on the proposed detectability notion, a UISE is designed via a moving horizon estimation strategy using a full-order model as well as past and current measurements. Next, we tighten this detectability notion to design a two-stage MHE-based UISE, which is computationally more efficient than the MHE-based UISE using full-order models. In a simulation example with a plant growth process, both variants of MHE-based UISEs are compared with a conventional MHE to illustrate the merits of the developed methods.