Name
Title | ||
|---|---|---|
Estimation With Unknown Inputs And Uncertainties For Sampled-Data Systems Based On Quasi Sliding Mode |
Abstract | ||
|---|---|---|
In this work, we consider the problem of simultaneously estimating the system states and unknown inputs in a linear sampled-data system, whose dynamics is influenced by external disturbances and uncertainties. Hardware limitations prevent an estimation scheme for a sampled-data system from achieving finite-time convergence, which is a typical property of existing sliding mode observers for dynamical continuous-time systems, because the sampling period is finite. Due to the sampling process, an approximate implementation of such an observer, designed for a continuous-time system, may not retain the desired performance in the sampled-data context. In this paper, we present an observer which takes advantage of the quasi-sliding motion concept to simultaneously estimate the state variables and the unknown input signals in a sampled-data context. A theoretical study is conducted to formally justify the convergence properties of the observer whilst simulation results are provided to show the efficiency of the proposed scheme. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1080/00207179.2020.1750706 | INTERNATIONAL JOURNAL OF CONTROL |
Keywords | DocType | Volume |
Sliding mode observer, sampled-data systems, unknown inputs, disturbances, uncertainty | Journal | 94 |
Issue | ISSN | Citations |
11 | 0020-7179 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thang Trung Nguyen | 1 | 40 | 8.03 |
| christopher edwards | 2 | 812 | 89.97 |
| Guido Herrmann | 3 | 83 | 11.31 |