Multibody dynamic systems as Bayesian networks: Applications to robust state estimation of mechanisms
| Autor: | José Luis Torres-Moreno, Antonio Giménez-Fernández, José Luis Blanco-Claraco |
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| Rok vydání: | 2014 |
| Předmět: |
Sequential estimation
Control and Optimization Computer science Mechanical Engineering Aerospace Engineering Bayesian network Computer Science Applications Extended Kalman filter Formalism (philosophy of mathematics) Control theory Modeling and Simulation Statistical inference State observer Graphical model Particle filter |
| Zdroj: | Multibody System Dynamics. 34:103-128 |
| ISSN: | 1573-272X 1384-5640 |
| DOI: | 10.1007/s11044-014-9440-9 |
| Popis: | This article addresses the problem of robustly estimating the dynamic state of a mechanism from a set of noisy sensor measurements. We start with a rigorous treatment of the problem from the perspective of graphical models, a popular formalism in the fields of statistical inference and machine learning. The modeling power of such a formalism is demonstrated by showing how the sequential estimation of a mechanism state with an extended Kalman filter (EKF), often used in previous works, becomes just one of the possible solutions. As an interesting alternative, we derive the formulation of a sequential Monte Carlo (SMC) filter, also known as a particle filter (PF), suitable for online tracking the state of a rigid mechanism. We validate our ideas with both simulated and real datasets. Moreover, we prove the usefulness of the particle filtering solution for real-work applications due to its unmatched capability of automatically inferring the initial states of the mechanism along with its “assembly configuration” or “branch” if several ones are possible, a feature not matched by any previously proposed state observer in the multibody literature. |
| Databáze: | OpenAIRE |
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