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Communication Dans Un Congrès Année : 2023

Robust moving-horizon estimation for quasi-LPV discrete-time systems

Résumé

This paper deals with moving horizon estimation for a class of quasi-LPV systems. Under both the observability condition and the incremental Exponential Input Output-to-State Stability~(i-EIOSS) assumption, novel convergence conditions of the Moving Horizon Estimator~(MHE) are proposed. Such conditions guarantee exponential robust stability of the MHE based on a particular prediction step that is independent of the dynamics of the system. To relax the observability condition, a new prediction equation is integrated into the MHE scheme. This prediction equation exploits the dynamics of the system and contains an additional correction term depending on the estimation error and a design parameter to be determined to ensure the exponential robustness of the MHE. An application to vehicle motion estimation, using the kinematic model, is provided to show the validity and effusiveness of the proposed method, and to support the theoretical results.
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Dates et versions

hal-04015324 , version 1 (05-03-2023)

Identifiants

  • HAL Id : hal-04015324 , version 1

Citer

Hasni Arezki, Angelo Alessandri, Ali Zemouche. Robust moving-horizon estimation for quasi-LPV discrete-time systems. 22nd IFAC World Congress, IFAC 2023, Jul 2023, Yokohama, Japan. ⟨hal-04015324⟩
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