This paper presents a study on developing a hybrid 3D position observer for a rover with acceleration and relative distance measurements. The observer design utilizes two different methodologies; a Trajectory Based Optimization Design (TBOD) and a Linear Matrix Inequality (LMI) method. We prove that, under the proposed solutions, the boundedness of the estimation error is guaranteed. The performance of the observer is evaluated and compared to a standard EKF using comprehensive Monte Carlo simulations.
Oliva, F., Mattogno, S., Tenaglia, A., Masocco, R., Martinelli, F., Carnevale, D. (2023). A Trajectory Based Optimization Approach for Hybrid Observer Design. In 2023 62nd IEEE Conference on Decision and Control (CDC) (pp.1873-1878). IEEE [10.1109/CDC49753.2023.10383599].
A Trajectory Based Optimization Approach for Hybrid Observer Design
Oliva F.
Membro del Collaboration Group
;Mattogno S.Membro del Collaboration Group
;Tenaglia A.Membro del Collaboration Group
;Masocco R.Membro del Collaboration Group
;Martinelli F.Membro del Collaboration Group
;Carnevale D.Membro del Collaboration Group
2023-01-01
Abstract
This paper presents a study on developing a hybrid 3D position observer for a rover with acceleration and relative distance measurements. The observer design utilizes two different methodologies; a Trajectory Based Optimization Design (TBOD) and a Linear Matrix Inequality (LMI) method. We prove that, under the proposed solutions, the boundedness of the estimation error is guaranteed. The performance of the observer is evaluated and compared to a standard EKF using comprehensive Monte Carlo simulations.| File | Dimensione | Formato | |
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