Motion control of deep sea vehicle 'OTOHIME': modeling with neural network. (17th December 2021)

• Record Type: Journal Article
• Authors: Fujii, S.; Tahara, J.; Zhang, F.; Koike, M.; Ohta, Y.; Watanabe, Y.
• Is Part Of: Advanced robotics. Volume 35:Number 24(2021)
• Page Start: 1500
• Page End: 1512
• Publication Date: 2021-12-17
• Subjects: Underwater robotics -- modeling -- neural network -- nonlinear autoregressive external -- long short-term memory; Robotics -- Periodicals; Robotics -- Japan -- Periodicals; Robotics; Japan; Periodicals; 629.89205
• DOI: 10.1080/01691864.2021.1985606
• Languages: English
• ISSNs: 0169-1864

Abstract

Abstract : Deep sea survey vehicle 'OTOHIME' is one of the underwater vehicle owned in JAMSTEC and its automatic navigation system is developing now. To construct the control system, vehicle motion should be modeled at first. In this study, we treated a dynamic equation model and two types of neural network models and compared their performance. The latter showed rather better results than the former. Then we decide to use the neural network model to design the control system as the next step. GRAPHICAL ABSTRACT: UF0001