Earticle

초록

영어

Along with the development of shipping business, ships are becoming bigger, faster and more intelligent. Thus better performance of maneuver is demanded. To research for better control strategies, it is necessary to adopt new control theories and techniques. The application of neural network techniques and backstepping algorithm in ship motion control became an important research area in recent years. Aiming at the nonlinear of ship motion, also for application of control strategy, control strategy based on the RBF neural network and backstepping algorithm is proposed. The strategy employs the RBF neural network to approximate and substitute the system, and employs adaptive law designed by backstepping algorithm to adjust the weight of the RBF neural network. Finally, the proposed strategy was applied in ship course tracking control simulation and the satisfying performances demonstrate the feasibility and effectiveness of the ship control strategy.

목차

Abstract
 1. Introduction
 2. Ship Motion Mathematical Model
  2.1. Norrbin Nonlinear Ship Motion Model
  2.2. Ship Model Parameter Selection
 3. The Design of the Ship Course Controller
 4. The Ship Course Control Simulation
  4.1. Simulation Research Overview
  4.2. Simulation Results Of PID Controller
  4.3. Simulation Results of Backstepping Controller
  4.4. Joining Interference
  4.5. Model Parameters Changing
 5. Conclusion
 References

키워드

저자

• Guoqing Xia [ College of Automation, Harbin Engineering University, Harbin 150001, China ]
• Tiantian Luan [ College of Automation, Harbin Engineering University, Harbin 150001, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605