1993 Fiscal Year Final Research Report Summary
RESEARCH ON INTRODUCTION OF ADAPTIVE PREVIEW CONTROL TO ROBOTS
| Project/Area Number |
04650354
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
計測・制御工学
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| Research Institution | FAC.OF ENGINEERING, HOKKAIDO UNIVERSITY |
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Principal Investigator |
TSUCHIYA Takeshi Fac.of Engineering, Hokkaido University, Professor, 工学部, 教授 (90001172)
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| Co-Investigator(Kenkyū-buntansha) |
EGAMI Tadashi Fac.of Engineering, Kanagawa University, Associated Professor, 工学部, 助教授 (40201363)
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| Project Period (FY) |
1992 – 1993
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| Keywords | PREVIEW CONTROL / ADAPTATION / ROBOT MANIPURATOR / TRAJECTORY PLANNING / FUZZY REASONING / NEURAL NETWORK |
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| Research Abstract |
It is well known that utilization of future information on desired signal or disturbance signal which the output of the control system must follows or reject makes the control performance considerably improved. "Preview control" is one of such control methods. Control system synthesis method for preview control system has been developed on the basis of optimal regulator theory by the authors. However, the preview action acts like feedfoward action for already designed feedback control system. Then, robustness of the total control system including preview compensation becomes low. Then, adaptive function for parameter variations of the controlled objects is necessary. In this research, the following points are considered.
[1]Design method of preview control system for a nonlinear controlled object on the basisi of "digital acceleration method" whichi has been proposed by the authors.
[2]Development of design method of adaptive action for preview control system.
[4]Study on trajectory planning of the robot manipulator.
[4]Sapplication of learning methods such as Fuzzy reasoning and Neural network to robotics.
In this research, the following results are obtained.
[1]New design method without utilization of the acceleration signal from the controlled object is developed for nonlinear controlled object on the basis of the digital acceleration method. While the previous method needs the acceleration signal which is very hard work for usual controlled object.
[2]Adaptation for parameter variations of the controlled object is obtained to some extent by means of introduction of Fuzzy reasoning and Neural network into the preview control system.
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