Parameter identification for nonlinear models from a state-space approach
Overview & Synopsis
A new approach to parameter estimation of dynamical models is proposed. Its objective is to approximate at best the different dynamics of the system, instead of approximating at best the system output in time. This leads to a weighting of the error depending on the samples location in the state-space and input space. A possible implementation is proposed and applied for estimating the parameters of a two degrees of freedom vehicle dynamics model. The proposed approach is shown to better approximate the fast transient dynamics, at the cost of a degraded performance on steady-states.
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hal.science)
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Document Information
| Document Title | Parameter identification for nonlinear models from a state-space approach |
|---|---|
| Author / Organization | Jules Matz, Abderazik Birouche, Benjamin Mourllion, Fethi Bouziani, Michel Basset |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2020 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-233910 |
| Original Source Link | Visit Source Page on hal.science » |