Name
Title | ||
|---|---|---|
Input design and online system identification based on Poisson moment functions for system outputs with quantization noise |
Abstract | ||
|---|---|---|
We study optimal input design and bias-compensating parameter estimation methods for continuous-time models applied on a mechanical laboratory experiment. Within this task we compare two online estimation methods that are based on Poisson moment functions with focus on quantized system outputs due to an angular encoder: The standard recursive least-squares (RLS) approach and a bias-compensating recursive least-squares (BCRLS) approach. The rationale is to achieve acceptable estimation results in the presence of white noise, caused by low-budget encoders with low resolution. The input design and parameter estimation approaches are assessed and compared, experimentally, resorting to measurements taken from a laboratory cart system. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/MED.2017.7984090 | 2017 25th Mediterranean Conference on Control and Automation (MED) |
Keywords | Field | DocType |
online system identification,Poisson moment functions,system outputs,quantization noise,optimal input design,bias-compensating parameter estimation,continuous-time models,mechanical laboratory experiment,bias-compensating recursive least-squares approach,BCRLS approach,white noise | Noise measurement,Computer science,Control theory,Control engineering,White noise,Encoder,Poisson distribution,Estimation theory,System identification,Quantization (signal processing),Recursion | Conference |
ISSN | ISBN | Citations |
2325-369X | 978-1-5090-4534-1 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mayr, S. | 1 | 0 | 0.68 |
| gernot grabmair | 2 | 3 | 0.87 |
| Johann Reger | 3 | 40 | 17.29 |