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
- 2011.10
- 수록면
- 1,576 - 1,581 (6page)
이용수
초록· 키워드
오류제보하기
A pneumatic actuator is a device that is capable of converting energy from a pressurized gas into motion. While motion can be created through other means, such as a hydraulic or electric motor, pneumatic actuators are safer, cheaper, and cleaner. Therefore, pneumatic actuators have been used widely in the field of industry automation. However, the compressibility of air and the inherent non-linearity of pneumatic actuators cause challenges in controlling accurately position of pneumatic actuators. This paper presents an accurate non-linear back-box model (NBBM) for identifying the dynamic behavior of pneumatic actuators. Once the optimized NBBM of the pneumatic actuator is obtained, it can give a generation of an effective solution for designing a position controller of that. Here, the NBBM is a multi-player perceptron neural network (MLPNN), whose parameters are optimized by using the Lervenberg-Marquardt Back Propagation (LMBP) algorithm. For the model verification, a pneumatic actuator was set up to investigate the dynamics of it as well as to generate the training data. Next, the advanced NBBM for the pneumatic actuator is performed with suitable inputs to estimate the cylinder piston displacement. Finally, the NBBM ability is evaluated by a comparison of the estimated and real pneumatic actuator performance.
목차
Abstract
1. INTRODUCTION
2. SYSTEM DESCRIPTION
3. NBBM FOR PNEUMATIC ACTUATORS
4. PNEUMATIC ACTUATORIDENTIFICATION USING NBBM
5. CONCLUSIONS
ACKNOWLEDGEMENTS
REFERENCES
참고문헌 (0)
참고문헌 신청이 논문의 저자 정보
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-ECN-0101-2014-569-000915245