A control structure that makes possible the integration of a kinematic controller and a neural network(NN) compensator for mobile manipulators is presented. The stability of the closed loop system and the boundness of tracking errors are proved using Lyapunov theory. The NN compensation scheme proposed in this work can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamic in the mobile manipulator. On-line NN parameter tuning algorithms do no require off-line learning yet guarantee small tracking errors and bounded control signals are utilized.
한국어
이동로봇팔의 역학 제어기와 신경회로망 보상기가 결합된 제어구조를 제안한다. 신경망 보상이 적응적이 고 추적오차와 파라미터 추정치가 유계가 되는 파라미터 동조 알고리듬과 안정도 증명을 제시한다. 신경망 비선형 보상기를 이동로봇팔에 시뮬레이션 함으로서 비선형성의 해로운 영향을 줄이는 효과를 보여준다.
목차
요약 Abstract Ⅰ. 서론 Ⅱ. 이동로봇팔 Ⅲ. 신경회로망 Ⅳ. 이동로봇팔의 신경회로망 보상 Ⅴ. 시뮬레이션 및 실험 Ⅵ. 결론 REFERENCES
Ever since next generation convergence technology became one of the most important industries in the nation, computing professionals have encountered a growing number of challenges. Along with scholars and colleagues in related fields, they have gathered in avariety of forums and meetings over the last few decades to share their knowledge, experiences and the outcome of their research. These exchanges have led to the founding of the International Next-generation Convergence technology (INCA) on December 1, 2015. INCA was registered as an incorporated association under the Ministry of Information and Communications. The main purpose of the organization is to improve our society by achieving the highest capability possible in next generation convergence technology.
간행물
간행물명
차세대융합기술학회논문지 [The Journal of Next-generation Convergence Technology Association]