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
- 학위논문
- 저자정보
- 지도교수
- 성윤경
- 발행연도
- 2021
- 저작권
- 조선대학교 논문은 저작권에 의해 보호받습니다.
이용수1
이 논문의 연구 히스토리 (3)
- 이 논문의 후속연구가 궁금하신가요?
- 연관 학술논문 또는 학술발표를 통해 보다 발전된 연구결과를 확인하실 수 있습니다.
- 이 논문의 연구 히스토리 확인하기
초록· 키워드
오류제보하기
In this paper, Input Shaper Command using Artifitial Neural Network was developed to control residual deflection when hoisting motion of the system occurs. Input Shaping technology has been applied to various industrial sites and flexible systems, but it is senisitive to modeling errors and is forced to move vertically, and there are problems of increasing working time and reducing efficiency. In addition, since the safety of workers is important, residual deflection should be small even in situations where movement in the hoisting direction occurs. Therefore, it is necessary to develop an input shaping command with less residual deflection even when modeling errors occur.
First, the input shaping commands with arfitifial neural network was analyzed when the actual flexble system contains non-linearity. A network using modeling dynamics for the Double-Pendulum System and a tow-mode input shaping command was propased. In addition, a network that can be applied to the operation of the Tower Crane has been developed using a nonlinear shaper.
Network’s training algorithm used to global optimal PSO algorithm, and the performance index was evaluated. It uses a new input shaper and compares it with a shaper that has secured robustness over a wide frequency range. The simulation was divided into large-scale and mini-scale, and experimentally evaluated and verified with a mini-scale system.
First, the input shaping commands with arfitifial neural network was analyzed when the actual flexble system contains non-linearity. A network using modeling dynamics for the Double-Pendulum System and a tow-mode input shaping command was propased. In addition, a network that can be applied to the operation of the Tower Crane has been developed using a nonlinear shaper.
Network’s training algorithm used to global optimal PSO algorithm, and the performance index was evaluated. It uses a new input shaper and compares it with a shaper that has secured robustness over a wide frequency range. The simulation was divided into large-scale and mini-scale, and experimentally evaluated and verified with a mini-scale system.
목차
LIST OF TABLES ⅤLIST OF FIGURES ⅨABSTRACT Ⅹ제 1 장 서 론 11.1 연구 배경 및 필요성 11.2 이론적 배경 21.2.1 Input Shaping Technique 21.2.1.1. Zero Vibration(ZV) Shaper 51.2.1.2. Zero Vibration Derivative(ZVD) Shaper 51.2.1.3. Unit Magnitude Zero Vibration(UMZV) Shaper 61.2.1.4. Input Shaper 성능비교 71.2.2. Particle Swarm Optimization(PSO) Algorithm 91.3 논문의 진행방향 10제 2 장 비선형 System에 대한 Neural Network Input Shaper의 분석 112.1 서론 122.2 Nonlinear System Input Shaper 132.2.1. System modeling 142.2.2. 가속도 제한을 고려한 Input Shaper 142.2.3. 구동기의 비선형 동적 특성을 고려한 Input Shaper 162.3 Neural Network Approach 182.3.1. Neural Network Configuration 192.3.2. Neural Network Training 212.4 Shaper 성능 평가 242.4.1. 가속도 제한 Shaper 시뮬레이션 252.4.2. 1st-order Shaper 시뮬레이션 282.5 실험적 검증 312.5.1. 가속도 제한 Shaper의 실험적 검증 322.5.2. 1st-order Shaper의 실험적 검증 352.6 결론 37제 3 장 Double Pendulum System에 대한 Neural Network Input Shaper의 개발 393.1 서론 403.2 Double-Pendulum System Input Shaper 413.2.1. System modeling 423.2.2. Two-Mode Input Shaper 443.3 Neural Network Approach 453.3.1. Neural Network Configuration 463.3.2. Neural Network Training 473.4 Shaper 성능 평가 513.5 실험적 검증 543.6 결론 58제 4 장 Tower Crane의 비선형에 대한 Neural Network Input Shaper의 개발 594.1 서론 604.2 Nonlinear Tower Crane System Input shaper 614.2.1. System modeling 624.2.2. 1차 구동기의 비선형성을 고려한 Input Shaper 644.3 Neural Network Approach 664.3.1. Neural Network Configuration 674.2.2. Neural Network Training 684.4 Shaper 성능 평가 724.5 실험적 검증 754.6 결론 79제 5 장 결론 및 향후 연구방향 81참 고 문 헌 82
최근 본 자료
댓글(0)
0