A moving horizon state and parameter estimation scheme with guaranteed robust convergence

Abstract

We propose a moving horizon estimation scheme for joint state and parameter estimation for nonlinear uncertain discrete-time systems. We establish robust exponential convergence of the combined estimation error subject to process disturbances and measurement noise. We employ a joint incremental input/output-to-state stability ($\delta$-IOSS) Lyapunov function to characterize nonlinear detectability for the states and (constant) parameters of the system. Sufficient conditions for the construction of a joint $\delta$-IOSS Lyapunov function are provided for a special class of nonlinear systems using a persistence of excitation condition. The theoretical results are illustrated by a numerical example.

Replaced by final version. Presented at IFAC World Congress 2023, Yokohama, Japan

References in corpus (1)

• A Lyapunov function for robust stability of moving horizon estimation

Cited by in corpus (2)

• Nonlinear moving horizon estimation for robust state and parameter estimation -- extended version
• Trajectory Based Observer Design: A Framework for Lightweight Sensor Fusion