| Project/Area Number |
17360197
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Control engineering
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| Research Institution | Kyoto University |
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Principal Investigator |
SUGIE Toshiharu Kyoto University, Graduate Shool of Informatics, Professor (80171148)
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| Co-Investigator(Kenkyū-buntansha) |
ISHIKAWA Masato Kyoto University, Graduate School of Informatics, Lecturer (20323826)
AZUMA Shun-ichi Kyoto University, Graduate School of Informatics, Assistant Professor (40420400)
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| Project Period (FY) |
2005 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥16,310,000 (Direct Cost: ¥15,200,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2007: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2006: ¥5,200,000 (Direct Cost: ¥5,200,000)
Fiscal Year 2005: ¥6,300,000 (Direct Cost: ¥6,300,000)
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| Keywords | constrained systems / input saturation / discrete-valued input / numerical optimization / tracking control / 安定解析 / 制約システム |
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| Research Abstract |
This study considers a class of constrained systems, which have amplitude saturation in input/state or are actuated by discrete-valued signals. The purpose of the research is to develop systematic design methods for such systems and to achieve satisfactory control performance with low resolution actuators. The following results have been obtained.
First, for linear systems with input-saturation, we have developed a stability analysis method and a reference shaping method which improves the tracking performance based on off-line optimization. Its effectiveness is demonstrated through various numerical examples.
Second, as for model predictive control methods which determine the control subject to the systems constraints based on online optimization, we have developed two types of the methods. One is to reduce the computation burden concerning online optimization by exploiting a priori knowledge of the plant. The other is to improve the plant model in an adaptive way during the model predictive control method. Their effectiveness is validated through simulations.
Third, as for discrete-valued input control, we have obtained several results. One of the main points is to utilize the dynamic quantizers which transform the original continuous-valued signals to the signals in the given discrete-valued set based on the past sequence of the both signals. We give a simple design method of such dynamic quantizers for linear continuous-time systems, and develop an optimal design method for discrete-time case which is based on the detailed information of the plant to be controlled. Many numerical and experimental results support the utility of such dynamic quantizers. Furthermore, a robust control method for a class of nonlinear systems has been presented.
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