| Project/Area Number |
18560435
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
Control engineering
|
|---|
| Research Institution | Toyota Technological Institute |
|---|
Principal Investigator |
KAWANISHI Michihiro Toyota Technological Institute, Faculty of Engineering, Associate Professor (00283870)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
ADACHI Kazuhiko Kobe University, Faculty of Engineering, Associate Professor (30243322)
|
|---|
| Project Period (FY) |
2006 – 2007
|
| Project Status |
Completed (Fiscal Year 2007)
|
| Budget Amount *help |
¥3,710,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥210,000)
Fiscal Year 2007: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2006: ¥2,800,000 (Direct Cost: ¥2,800,000)
|
|---|
| Keywords | Bilinear matrix inequality / Branch and bound method / Real-coded genetic algorithm / Beowulf cluster computer / Parallel distributed calculation / Simultaneous optimization / BMI / BMI |
|---|
| Research Abstract |
1. Research for BMI optimization with Beowulf cluster
(1) A Beowulf cluster computer that enables us to achieve 2[decade] accuracy with 104 CPUs is constructed.
(2) On branch and bound method for BMI global optimization, we developed a novel branching operation considering complicating variable dimension which is important on practical problems.
(3) For the parallelization of the BMI branch and bound method, we developed parallelized algorithms that realize variable computational granularity. The developed algorithms of the BMI branch and bound are implemented in Beowulf cluster.
(4) On real-coded genetic algorithm for BMI optimization, we introduced a reduced-order individual expression and an individual evaluation method using LMI optimization. The developed method enables us to more accurately evaluate individuals compared to conventional eigenvalue-based methods.
(5) In order to reduce the computation burden of the developed method (4), we also developed a parallelization algorithm that fully utilizes the computational power of Beowulf cluster.
2. Research for simultaneous optimization based on BMI optimization method
(1) Parallel-link system is one of the best application of simultaneous optimization due to the complexity of the link structure. The performance of the parallel-link system depends on both controller and link-structure. For redundant parallel-link system, we developed a optimal power distribution control with LMI optimization. However, the globally optimal link-structure and globally optimal control row are not yet obtained.
(2) In the practical view point, modeling and parameter tuning are both important for simultaneous optimization design. In the context, we developed a data-based controller design method based on unfalsified control technique and support vector machine. The method clarified the possibility of the data-based simultaneous optimization method.
|
