NDLI: Neural networks based parking control of deep-sea HydroThermal Plume Explorer

Content Provider	IEEE Xplore Digital Library
Author	Xiujun Sun Yanhui Wang Hongwei Zhang Yan Yang
Copyright Year	2009
Description	Author affiliation: Department of Mechanical Engineering, University of Tianjin, NO.92, Weijin Road, Nankai District, China (Xiujun Sun; Yanhui Wang; Hongwei Zhang; Yan Yang)
Abstract	Depth parking control is important for the HTPE (HydroThermal Plume Explorer). It aims to have a more accurate parking depth and a less oscillation at the target depth. The plant approaching and motion forecasting model based on RBF neural network is built to self-adjust and approach online motion law of HTPE in ideal circumstance of the sea, and simulate output of given control quantity after an PID control period. Together with RBF neural network model, BP neural network based self-adaptive PID control model is used for rectification of PID parameters and for holding the parking depth. Finally, simulations testify that neural networks based control method is feasible.
Starting Page	4458
Ending Page	4462
File Size	5286550
Page Count	5
File Format	PDF
ISBN	9781424426928
DOI	10.1109/ICMA.2009.5244858
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Motion control Three-term control Payloads Predictive models Oceans Control systems Neck Lenses Sea measurements PID Depth parking control RBF neural network BP neural network
Content Type	Text
Resource Type	Article

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