Optimal design of two-degree-freedom control systems with preview feedforward based on noncausal inversion
| Project/Area Number |
18560444
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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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 | Chubu University |
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Principal Investigator |
SOGO Takuya Chubu University, college of engineering, associate professor (40273487)
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| Project Period (FY) |
2006 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥2,790,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥390,000)
Fiscal Year 2007: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2006: ¥1,100,000 (Direct Cost: ¥1,100,000)
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| Keywords | preview feedforward control / noncausal inversion / sampled-data systems / unstable zero / inverse problem / integral equation |
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| Research Abstract |
For this research project, it was investigated that the design problem of an infinite-time preview feedforward controller from the viewpoint of the model-matching problem based on the two-sided Laplace transform. It was shown that the so-mlled preview-based stable inversion for linear time-invariant systems is equivalent to the noncalisal solution to the model-matching problem. The preview feedforward controller can be easily discussed in the frequency domain where the design method for the optimal feedback controller has already been established. Moreover, with the same approach as stable inversion, we derived a transfer-function representation for the algorithm of adjoint-based iterative learning control. This indicates that the algorithm can be applied to infinite-dimensional systems such as flexible structures. This was demonstrated through an experiment on the tip control of a flexible arm.
Next, preview feedforward control for sampled-data systems was investigated. Since the transfer function of sampled-data systems with a short sampling period frequently has zeros outside the unit circle, it is recognized that iterative learning control (ILC), which is an iterative approach to obtain an inversion of a system, cannot be applied to such systems. In order to overcome this difficulty, we introduced a non-causal bounded inversion to a sampled-data system and investigate the limiting property of the inversion. It was demonstrated that a simple relation between the n-th differential operator and n Euler operators that are included in the pulse transfer function of the n-th integral element. Based on this relation, it was proved that the inversion of the sampled-data system approximates its continuous-time counterpart in a non-causal framework. This result suggests that ILC can be applied to sampled-data systems in this framework.
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Report
(3 results)
Research Products
(11 results)
