Details
| Original language | English |
|---|---|
| Article number | 110147 |
| Journal | AUTOMATICA |
| Volume | 138 |
| Early online date | 2 Feb 2022 |
| Publication status | Published - Apr 2022 |
| Externally published | Yes |
Abstract
Most available methods for joint state-parameter estimation in discrete-time stochastic time-varying systems suffer from severe performance degradation if the system is not persistently excited. We revisit and extend a recently proposed adaptive Kalman filter in order to enhance its robustness against periods of poor excitation. A Levenberg–Marquardt-like regularization algorithm is integrated in the filter to preclude the estimator from becoming unreliable due to wind-up effects. It is proven that, under uniform complete observability–controllability conditions and a persistent excitation condition, the expectations of the state and parameter estimation errors tend to zero exponentially fast. Furthermore, their stability (in the sense of Lyapunov) is guaranteed even if the persistent excitation condition is not satisfied. The effectiveness of the algorithm is demonstrated using real sensor data from a vehicle motion estimation application. Unlike the original algorithm, the proposed regularized adaptive Kalman filter provides accurate and reliable estimates of the states and parameters despite periods of poor excitation.
Keywords
- Adaptive observer design, Automotive vehicles, Discrete-time stochastic systems, Joint state and parameter estimation, Motion estimation, Non-persistently excited systems
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Electrical and Electronic Engineering
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In: AUTOMATICA, Vol. 138, 110147, 04.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Regularized adaptive Kalman filter for non-persistently excited systems
AU - Rodrigo Marco, Vicent
AU - Kalkkuhl, Jens C.
AU - Raisch, Jörg
AU - Seel, Thomas
PY - 2022/4
Y1 - 2022/4
N2 - Most available methods for joint state-parameter estimation in discrete-time stochastic time-varying systems suffer from severe performance degradation if the system is not persistently excited. We revisit and extend a recently proposed adaptive Kalman filter in order to enhance its robustness against periods of poor excitation. A Levenberg–Marquardt-like regularization algorithm is integrated in the filter to preclude the estimator from becoming unreliable due to wind-up effects. It is proven that, under uniform complete observability–controllability conditions and a persistent excitation condition, the expectations of the state and parameter estimation errors tend to zero exponentially fast. Furthermore, their stability (in the sense of Lyapunov) is guaranteed even if the persistent excitation condition is not satisfied. The effectiveness of the algorithm is demonstrated using real sensor data from a vehicle motion estimation application. Unlike the original algorithm, the proposed regularized adaptive Kalman filter provides accurate and reliable estimates of the states and parameters despite periods of poor excitation.
AB - Most available methods for joint state-parameter estimation in discrete-time stochastic time-varying systems suffer from severe performance degradation if the system is not persistently excited. We revisit and extend a recently proposed adaptive Kalman filter in order to enhance its robustness against periods of poor excitation. A Levenberg–Marquardt-like regularization algorithm is integrated in the filter to preclude the estimator from becoming unreliable due to wind-up effects. It is proven that, under uniform complete observability–controllability conditions and a persistent excitation condition, the expectations of the state and parameter estimation errors tend to zero exponentially fast. Furthermore, their stability (in the sense of Lyapunov) is guaranteed even if the persistent excitation condition is not satisfied. The effectiveness of the algorithm is demonstrated using real sensor data from a vehicle motion estimation application. Unlike the original algorithm, the proposed regularized adaptive Kalman filter provides accurate and reliable estimates of the states and parameters despite periods of poor excitation.
KW - Adaptive observer design
KW - Automotive vehicles
KW - Discrete-time stochastic systems
KW - Joint state and parameter estimation
KW - Motion estimation
KW - Non-persistently excited systems
UR - http://www.scopus.com/inward/record.url?scp=85123854790&partnerID=8YFLogxK
U2 - 10.1016/j.automatica.2021.110147
DO - 10.1016/j.automatica.2021.110147
M3 - Article
AN - SCOPUS:85123854790
VL - 138
JO - AUTOMATICA
JF - AUTOMATICA
SN - 0005-1098
M1 - 110147
ER -