| Project/Area Number |
16500114
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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 |
Perception information processing/Intelligent robotics
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| Research Institution | University of the Ryukyus |
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Principal Investigator |
KINJO Hiroshi University of the Ryukyus, Department of Engineering, Associate Professor, 工学部, 助教授 (50211206)
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| Co-Investigator(Kenkyū-buntansha) |
YAMAMOTO Tetsuhiko University of the Ryukyus, Department of Engineering, Professor, 工学部, 教授 (20045008)
NAKAZONO Kunihiko University of the Ryukyus, Department of Engineering, Research Assistant, 工学部, 助手 (80284959)
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| Project Period (FY) |
2004 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2005: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 2004: ¥600,000 (Direct Cost: ¥600,000)
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| Keywords | Learning machine / Mechanical dynamics and control / Intelligent control / Neural networks / Non-holonomic systems / Genetic algorithms / GA学習法 / ニューロ制御器 / 制御系設計法 |
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| Research Abstract |
The main object of this research project is to construct control system for non-holonomic systems using neurocontroller (NC) based on a genetic algorithm (GA). One method for the nonholonomic system controller design is the time-state control form that utilizes a chained form conversion. The chained forms are powerful and useful for designing the nonholonomic control system. However, the time-state control form has some limitations in the controllable ranges due to the conversion. In this research, we propose a design method of a state feedback controller for a nonholonomic system using an NC without chained forms. The NC is trained by a genetic algorithm. In the controller design, the abilities of pattern recognition and generalization of the neural network are utilized. In the GA process, NCs are evaluated on the basis of control performance in which the squared errors that result from the control simulations starting from all the initial states are calculated. Based on the control performance, NCs are evolved through the GA processes. Results of simulations show that the NCs trained using a GA exhibit good control performance of some example objects of the nonholonomic systems. One of the control strategies of the NC resembles that of time-state control form. The proposed method has no limitations in the controllable ranges in the initial states.
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