Foundation of control theory based on statistical data analysis
| Project/Area Number |
25420437
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Research Field |
Control engineering/System engineering
|
|---|
| Research Institution | Kyoto University |
|---|
Principal Investigator |
|
|---|
| Project Period (FY) |
2013-04-01 – 2017-03-31
|
| Project Status |
Completed (Fiscal Year 2016)
|
| Budget Amount *help |
¥5,070,000 (Direct Cost: ¥3,900,000、Indirect Cost: ¥1,170,000)
Fiscal Year 2015: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2014: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2013: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
|
|---|
| Keywords | Control theory / System identification / Statistical learning / Nonlinear control / ベイズ推定 / 確率最適制御 / 統計的学習 |
|---|
| Outline of Final Research Achievements |
This research program mainly achieves three major results by applying statistical learning algorithms to some problems in control engineering. One is system identification for transfer functions and state space models based on variational Bayes. Another one is active learning for system identification based on frequency weighted mutual information. The last one is to use Gaussian process inference for nonlinear controller design. In control engineering, most of the design procedures are based on a mathematical model of the plant system. However, it is not so easy to obtain precise mathematical models in practice. The scope of this research program is to derive a new design algorithms based on statistical learning theory which were not used in control engineering research area so far.
|
Report
(5 results)
Research Products
(37 results)
