2002 Fiscal Year Final Research Report Summary
Joint design of model set identification and learning type control
| Project/Area Number |
12450171
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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 Graduate School of Informatics, Professor, 情報学研究科, 教授 (80171148)
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| Co-Investigator(Kenkyū-buntansha) |
FUJIMOTO Kenji Graduate School of Informatics, Research Associate, 情報学研究科, 助手 (10293903)
OSUKA Koichi Graduate School of Informatics, Associate Professor, 情報学研究科, 助教授 (50191937)
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| Project Period (FY) |
2000 – 2002
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| Keywords | model-set identification / learning control / mechanical systems / nonlinear control |
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| Research Abstract |
As for the joint design of model-set identification and learning type control, we have obtained the following results.
Concerning to the model-set identification, one of the difficulties is that the framework of model set-identification is not consistent with the traditional stochastic approach of parameter identification. As a result, the obtained model set tends to be conservative. We have proposed identification methods which obtain model sets by taking the stochastic properties such as independency between noises and output signals into account. This overcomes the shortcoming partially. The effectiveness is evaluated through experiments using flexible structures.
Next, we have considered a class of nonlinear systems which are called Hamiltonian systems. This class contains a combination of mechanical systems and electrical systems. We have clarified that the inherent structures such as passivity and adjoint systems, which form a basis of applicability of learning control to this class of systems.
As for learning control, major demerits of the existing methods is that they have to use differential of error signals when the precise knowledge of the plants is not available. We have solved this problem in a couple of ways. One is to restrict the input space into a prescribed finite input signals when we adopt an iterative learning control, which turns out to be related to model identification very closely. The other is to use I/O signals of the adjoint systems of Hamiltonian systems in order to calculate its gradient with respect to given cost functions. The point here is that we can achieve this without any model parameters. The usefulness of both methods are demonstrated through experiments using nonlinear manipulators. We also have developed a new method of iterative feedback tuning which is robust against frictions.
The development of more effective way of combining learning control with model-set identification is left as a future research work.
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