{"title":"Adaptive Autopilot System for Small Ships Employing E-TOP Method and SOM","authors":"H. Maeno, T. Yamakawa","doi":"10.1109/INDIN.2006.275714","DOIUrl":null,"url":null,"abstract":"A phase diagram of error and its derivative is useful to illustrate the state of the control system, where a fixed point at origin stands for a stable state, a point leaving origin an unstable state, a point moving round the origin an oscillatory state, etc. Thus the state of the control system is estimated by the motion of the point in the diagram. When the control system is under a strong or periodic disturbance, the state point moves round the origin staggeringly or periodically. It is difficult to evaluate the state of the control system in such conditions and the appropriate control is hardly realized. The E-TOP (evaluation with trajectory on phase diagram) method, presented by the authors, facilitates extracting the feature parameters from the trajectory in the phase diagram. The feature parameters characterize the behaviour of the control system. The adaptive PID autopilot system employing the E-TOP method is very efficient to evaluate the behaviour of small ships, the attitude of which is easily varied by the waves. However, when the attitude of the ship body is strongly staggering, the adaptive autopilot system cannot work appropriately. To cope with this problem, we propose the advanced E-TOP adaptive PID autopilot system, which employs the self-organizing maps (SOM)[2]. The advantage of this autopilot system to the conventional one is shown by the software simulation.","PeriodicalId":120426,"journal":{"name":"2006 4th IEEE International Conference on Industrial Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 4th IEEE International Conference on Industrial Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDIN.2006.275714","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
A phase diagram of error and its derivative is useful to illustrate the state of the control system, where a fixed point at origin stands for a stable state, a point leaving origin an unstable state, a point moving round the origin an oscillatory state, etc. Thus the state of the control system is estimated by the motion of the point in the diagram. When the control system is under a strong or periodic disturbance, the state point moves round the origin staggeringly or periodically. It is difficult to evaluate the state of the control system in such conditions and the appropriate control is hardly realized. The E-TOP (evaluation with trajectory on phase diagram) method, presented by the authors, facilitates extracting the feature parameters from the trajectory in the phase diagram. The feature parameters characterize the behaviour of the control system. The adaptive PID autopilot system employing the E-TOP method is very efficient to evaluate the behaviour of small ships, the attitude of which is easily varied by the waves. However, when the attitude of the ship body is strongly staggering, the adaptive autopilot system cannot work appropriately. To cope with this problem, we propose the advanced E-TOP adaptive PID autopilot system, which employs the self-organizing maps (SOM)[2]. The advantage of this autopilot system to the conventional one is shown by the software simulation.
误差及其导数的相位图有助于说明控制系统的状态,其中原点处的固定点代表稳定状态,离开原点的点代表不稳定状态,绕原点移动的点代表振荡状态,等等。因此,控制系统的状态是由图中点的运动来估计的。当控制系统处于强扰动或周期性扰动下时,状态点会以交错或周期性的方式绕原点运动。在这种情况下,控制系统的状态难以评估,难以实现适当的控制。本文提出的E-TOP (evaluation with trajectory on phase diagram)方法便于从相图中的轨迹中提取特征参数。特征参数表征控制系统的行为。采用E-TOP方法的自适应PID自动驾驶系统对姿态易受海浪影响的小型船舶的行为评估非常有效。然而,当船体姿态发生强烈的摇晃时,自适应自动驾驶系统无法正常工作。为了解决这个问题,我们提出了先进的E-TOP自适应PID自动驾驶系统,该系统采用自组织地图(SOM)[2]。软件仿真显示了该自动驾驶系统相对于传统自动驾驶系统的优越性。