Ship Attitude Prediction Based on Input Delay Neural Network and Measurements of Gyroscopes

Yunlong Wang, Mohsen N. Soltani, Dil muhammed Akbar Hussain

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

7 Citations (Scopus)


Due to the uncertainty and random nature of ocean waves, the accurate prediction of ship attitude is hard to be achieved, especially in high sea states. A ship attitude prediction method using Input Delay Neural Network (IDNN) is proposed in this paper. One of the advantages of this method is that it takes the measurements of Microelectromechanical Systems (MEMS) gyrosocpes, besides ship Euler angles, as the inputs of IDNN, which can greatly increase the prediction precision of ship attitude with little increase in system cost. The effectiveness of proposed method is validated through a data set sampled in a ship simulation hardware system. Moreover, the factors that affect the prediction performance are also explored through a set of experiments. The prediction method proposed can achieve high precision, that is, the root-mean-square prediction errors for roll, pitch and yaw, are 0.26 deg, 0.12 deg and 0.26 deg, respectively, when the prediction time is 2 sec. This precision is high enough for most attitude stabilization control systems.
Original languageEnglish
Title of host publicationProceedings of 2017 American Control Conference (ACC)
Number of pages7
PublisherIEEE Press
Publication dateMay 2017
ISBN (Electronic)978-1-5090-5992-8
Publication statusPublished - May 2017
Event2017 American Control Conference: 2017 American Control Conference - Sheraton Seattle Hotel, Seattle, United States
Duration: 24 May 201726 May 2017


Conference2017 American Control Conference
LocationSheraton Seattle Hotel,
Country/TerritoryUnited States
Internet address


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