Iterartive control systems design based on experimental data
| Project/Area Number |
08455195
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (B)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
計測・制御工学
|
|---|
| Research Institution | KYOTO UNIVERSITY |
|---|
Principal Investigator |
SUGIE Toshiharu Kyoto University, Graduate School of Engineering, Professor, 工学研究科, 教授 (80171148)
|
|---|
| Project Period (FY) |
1996 – 1997
|
| Project Status |
Completed (Fiscal Year 1997)
|
| Budget Amount *help |
¥6,600,000 (Direct Cost: ¥6,600,000)
Fiscal Year 1997: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 1996: ¥5,700,000 (Direct Cost: ¥5,700,000)
|
|---|
| Keywords | Integration of identification and control / Robust control / System identification / Convex optimization / Control of Helicopter / ヘリコプタ制御 / 部分空間同定法 / 線形行列不等式 / 数値最適化 / 統合化設計 |
|---|
| Research Abstract |
The following results have been achieved in the current year by developping the results of(i)System indentification for robust control and(ii)Numerical optimization which were done in the previous year.
*1.System identification for robust control*
A unified approach of system identification and design of control systems has been obtained based on the results on subspace state-space identification which were done in the previous year. Since robust control needs a set of perturbed models, we proposed one method of identifying such a set which reduces the conservativeness by considering statistical properties of the added noise.
*2.Design of control systems via numerical optimization*
We exploit a method of mu analysis and synthesis, which play an important role in the robust control to handle physical parameter perturbation, via parameter dependent LMI(linear matrix inequality). It is expected that the proposed method will improve the performance of the control systems.
A study on a method of constructing a control system directly from input-output data without the knowledge plant model are discussed, and one such learning method by convex optimization is proposed.
*3.Experiment of controlling a helicopter*
System identification of the 4-input 6-output helicopter system, which was manufactured in the previous year, have been done by dividing it into for sub-systems. After a basic experiment via H_* control, we re-design a controller by considering the interference among the sub-systems, and then we have obtained a good result in an experiment of havering and tacking of a circle trajectory. However, the unified result of system identification and design of control systems were not achieved because of insufficiency of the sensor precision etc.
|
Report
(3 results)
Research Products
(9 results)
