지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2009.8
- 수록면
- 2,091 - 2,096 (6page)
이용수
이 논문의 연구 히스토리 (2)
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초록· 키워드
오류제보하기
This paper presents two kinds of adaptive control schemes for robot manipulator which has the parametric uncertainties. In order to compensate these uncertainties, we use the NN (neural network system) that has the capability to approximate any nonlinear function over the compact input space. In the proposed control schemes, we need not derive the linear formulation of robot dynamic equation and tune the parameters. We also suggest the robust adaptive control laws in all proposed schemes for decreasing the effect of approximation error. To reduce the number of neural of network, we consider the properties of robot dynamics and the decomposition of the uncertainty function. The proposed controllers are robust not only to the structured uncertainty such as payload parameter, but also to the unstructured one such as friction model and disturbance. The validity of the control scheme is shown by computer simulations and experiment of dual-arm robot manipulator.
목차
Abstract
1. INTRODUCTION
2. NEURAL NETWORK SYSTEM
3. PROPERTIES OF ROBOT DYNAMIC MODEL AND UNCERTAINTIES
4. ADAPTIVE CONTROL OF ROBOT MANIPULATOR USING NEURAL NETWORK COMPENSATOR
5. NEURAL REDUCTION ALGORITHM
6. SIMULATION AND EXPERIMENT
7. CONCLUSIONS
REFERENCES
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