Affiliation: | 1. School of Electronic Information, Wuhan University, Wuhan 430072, China;2. China Ship Development and Design Center, Wuhan 430064, China;3. Institute of Artificial Intelligence and Robotics (IAIR), Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, Hunan, China;1. School of Mechanical Engineering, University of Shanghai for Science and Technology, China;2. School of Mechanical Engineering, Jiangsu University, China;3. College of Engineering, Coventry University, Coventry, UK;4. School of Physics, Engineering & Computer Science, University of Hertfordshire, UK;1. School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ 85281, USA;2. Federal Aviation Administration, Atlantic City Int’l Airport, NJ 08405, USA |
Abstract: | The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems. |