[논문]Design of an RCGA-based Linear Active Disturbance Rejection Controller for Ship Heading Control

Ahn, Jong-Kap; So, Myung-Ok

doi:10.5394/kinpr.2020.44.5.423

Abstract ▼ AI-Helper

A ship's automatic steering system is the basis for addressing control difficulties related to course-changing and course-keeping during navigation through heading angle control, and is a link in realizing unmanned and autonomous ships. This study proposes a robust RCGA-based linear active disturban...

A ship's automatic steering system is the basis for addressing control difficulties related to course-changing and course-keeping during navigation through heading angle control, and is a link in realizing unmanned and autonomous ships. This study proposes a robust RCGA-based linear active disturbance rejection controller (LADRC) design method considering environmental disturbances, measurement noise, and model uncertainties in designing a ship heading controller for use when the ship is sailing. The LADRC consisted of a transient profile, a linear extended state observer, and a PD controller. The control gains in the LADRC with the linear extended state observer were adjusted by RCGAs to minimize the integral of the time-weighted absolute error (ITAE), which is an evaluation function of the control system. The proposed method was applied to ship heading control, and its effectiveness was validated by comparing the propulsive energy loss between the proposed method and a conventional linear PD controller. The simulation results showed that the proposed method had the advantages of lower propulsive energy loss, more robustness, and higher tracking precision than the conventional linear PD controller.

주제어

표/그림 (10)

그림 Fig. 1 Autopilot system
그림 Fig. 2 RCGAs based LADRC gains tuning
그림 Fig. 3 LADRC control system for ship heading control
표 Table 1 Coefficients of characteristics of model dynamics and performance index
그림 Fig. 4 Transient response of LADRC and PD controller
그림 Fig. 5 Transient response of LADRC and PD controller with internal uncertainty
표 Table 2 Performance of transient response of LADRC and PD controller
표 Table 3 Performance of transient response of LADRC and PD controller with internal uncertainty
그림 Fig. 6 Course changing 0[°]→60[°] and course keeping for LADRC and PD controller with disturbance
그림 Fig. 7 Course keeping 0[°] for LADRC and PD controller with sensor noise

AI 본문요약
AI-Helper

* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.

가설 설정

To be more specific, PID controllers have four fundamental difficulties. 1) The control signal jumps suddenly to obtain an output that follows most of the settings entered in a stepwise ways, which is not suitable for most dynamic systems. 2) PID controllers are often implemented without derivative terms due to the effects of noise.
1) The control signal jumps suddenly to obtain an output that follows most of the settings entered in a stepwise ways, which is not suitable for most dynamic systems. 2) PID controllers are often implemented without derivative terms due to the effects of noise. 3) The sum of the weights in Eq.

제안 방법

A simulation was performed applying the proposed method to ship heading control, and the effectiveness was validated by comparison with a conventional linear PD controller based on a performance evaluation index derived from the perspective of propulsive energy loss.
As a result of applying the proposed method to ship heading control, performing simulation, and evaluating it based on the performance evaluation index derived from the perspective of propulsive energy loss, the proposed method showed significantly improved results compared to the PD controller. In particular, when there are environmental disturbances (such as wind and wave) and sensor noise, the PD controller does not track the setting course well, but the proposed method tracks the setting course in a satisfying manner. Although not shown in the paper, the proposed controller also deviates from the setting course when there is more than a certain amount of noise and disturbance, and further studies should be performed in the future to minimize the effect of such noise and disturbance.
In controller design, focusing too much on setting value tracking performance results in poor disturbance reject performance, and vice versa, so there is a need for some compromise between each other. Ships are constantly subjected to various disturbances and uncertainties on the service voyage, so this study proposed a LADRC design method that focuses on improving disturbance elimination performance for ship heading control. The gains of the controller in the LADRC with linear extended state observer were adjusted by RCGAs to minimize the ITAE which is the objective functions.
8) from a gradient-like reproduction, crossover probability (95%) from a modified simple crossover, and mutation probability (20%) by a dynamic mutation. This study also used linear scaling and elitism strategies to enhance the search performance.
This study proposes an RCGA-based Linear Active Disturbance Rejection Controller (LADRC) design method that is robust against disturbances and noise during navigation and model uncertainties in designing the controller for ship heading control. The LADRC (Han, 2009) consists of a transient profile, a linear extended state observer, and a PD controller, and it treats the disturbances and internal uncertainties applied to the system as total disturbances, and estimates and compensates in real-time.

성능/효과

The gains of the controller in the LADRC with linear extended state observer were adjusted by RCGAs to minimize the ITAE which is the objective functions. As a result of applying the proposed method to ship heading control, performing simulation, and evaluating it based on the performance evaluation index derived from the perspective of propulsive energy loss, the proposed method showed significantly improved results compared to the PD controller. In particular, when there are environmental disturbances (such as wind and wave) and sensor noise, the PD controller does not track the setting course well, but the proposed method tracks the setting course in a satisfying manner.
5 shows the transient response characteristics of the modeling errors and the internal uncertainties of the plant. The modeling errors of the plant to be controlled were T₁*(+10%), T₂*(-10%), T₃*(+10%), K*(-10%), and T_E*(-10%), and the ship speed was changed to 13.6 [knot], -20% of 17 [knot]. Table 3 summarizes the transient response characteristics and propulsive energy loss.

후속연구

In particular, when there are environmental disturbances (such as wind and wave) and sensor noise, the PD controller does not track the setting course well, but the proposed method tracks the setting course in a satisfying manner. Although not shown in the paper, the proposed controller also deviates from the setting course when there is more than a certain amount of noise and disturbance, and further studies should be performed in the future to minimize the effect of such noise and disturbance.

참고문헌 (10)

Chen Z., Qin B., Sun M. and Sun Q.(2019), "Q-learning-based parameters adaptive algorithm for active disturbance rejection control and its application to ship course control", Neurocomputing, 408, pp. 51-63.
Fossen T. I.(2002), Marine Control Systems-Guidance. Navigation, and Control of Ships, Rigs and Underwater Vehicles, Marine Cybernetics.
Han, J.(2009), "From PID to Active Disturbance Rejection Control", IEEE transactions on Industrial Electronics, Vol. 56, No. 3, pp. 900-906.

상세보기
He, W., Yin, Z. and Sun, C.(2017), "Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function", IEEE Transactions on Cybernetics, vol. 47, no. 7, pp. 1641-1651.

상세보기
Larrazabala, M. J. and Penasb, S. M.(2016), "Intelligent rudder control of an unmanned surface vessel", Expert Systems with Applications, Volume 55, 15, pp 106-117.

상세보기
Liu, H., Shao, C., Ma, N. and Gu, X. C.(2017a), "Ship Course Planning and Course Keeping in Close Proximity to Banks Based on Optimal Control Theory", Proceedings of the 12th International Conference on Marine Navigation and Safety of Sea Transportation (TransNav 2017), pp. 85-92.
Liu, Z.(2017b), "Ship Adaptive Course Keeping Control With Nonlinear Disturbance Observer", IEEE Access, Vol. 5, pp. 17567-17575

상세보기
Pathan, D. M., Unar, M. A. and Memon, Z. A.(2012), "Fuzzy Logic Trajectory Tracking Controller for a Tanker", Mehran University Research Journal of Engineering & Technology, Vol. 31, No. 2, pp. 315-324.
Sohn, K. and Lee, G.(1995), "On a Performance Index of Automatic Steering System of Ships", Journal of the Society of Naval Architects of Korea, Vol. 32, No. 4, pp. 27-37.
Zwierzewicz, Z.(2014), "On the ship course-keeping control system design by using robust and adaptive control", 19th International Conference on Methods and Models in Automation and Robotics, pp. 189-194.

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증

Design of an RCGA-based Linear Active Disturbance Rejection Controller for Ship Heading Control 원문보기

Abstract ▼ AI-Helper

주제어

표/그림 (10)

표/그림 (10)

AI 본문요약
AI-Helper

가설 설정

제안 방법

성능/효과

후속연구

참고문헌 (10)

이 논문을 인용한 문헌

관련 콘텐츠

원문 보기

원문 URL 링크

오픈액세스(OA) 유형

이 논문과 함께 이용한 콘텐츠

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

선택된 텍스트

연합인증

Design of an RCGA-based Linear Active Disturbance Rejection Controller for Ship Heading Control 원문보기

Abstract ▼ AI-Helper

주제어

표/그림 (10) 모든 표/그림 보기

표/그림 (10) 슬라이드로 보기

AI 본문요약 엑셀 다운로드 AI-Helper

가설 설정

제안 방법

성능/효과

후속연구

참고문헌 (10)

이 논문을 인용한 문헌

관련 콘텐츠

원문 보기

원문 URL 링크

오픈액세스(OA) 유형

이 논문과 함께 이용한 콘텐츠

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

선택된 텍스트

표/그림 (10)

표/그림 (10)

AI 본문요약
AI-Helper