| Project/Area Number |
14550442
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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 | Kanazawa University |
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Principal Investigator |
FUJITA Masayuki Kanazawa University, Faculty of Engineering, Professor, 工学部, 教授 (90181370)
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| Co-Investigator(Kenkyū-buntansha) |
AZUMA Takehito Kanazawa University, Faculty of Engineering, Research Associate, 工学部, 助手 (60308179)
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| Project Period (FY) |
2002 – 2003
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| Project Status |
Completed (Fiscal Year 2003)
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| Budget Amount *help |
¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 2003: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2002: ¥1,300,000 (Direct Cost: ¥1,300,000)
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| Keywords | Model Predictive Control / Nonlinear Control / Visual Servo / Visual Feed back / Robot Control / Robust Control / H_∞ Control / Digital Control / H_∞制御 |
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| Research Abstract |
In this research, we consider the design of observer-based dynamic visual servo systems via nonlinear H_∞ model predictive control. Firstly, the 3-D visual feedback control problem is established as the robot control problem and the estimation problem of the relative rigid body motion between the camera and the target object. For this problem, we propose a nonlinear H_∞ model l)redicti~7e control law for the visual servo systems. Specifically, the appropriate energy function to the proposed control law plays an important role in the control design.
Secondly, the dynamic visual servo system is constructed from a robot manipulator, a camera and an image processing board with a. high-performance DSP and a. real-time system. While we provide real-time software for implementing the proposed control law to the real system, the algorithm for optimal problems is considered in order to calculate the controller by the real system.
Finally, we have experiments for evaluating the control performances using the constructed dynamic visual servo system. Experimental results are analyzed exactly by a high-performance workstation. Not only the states of the average but also the states of the worst case are considered in order to verify the proposed control law with experimental results systematically. Moreover, the real-time software and the algorithm for optimal problems are reconsidered via, simulations and experiments.
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