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

Author

Sun, Xiujun ; Wang, Yanhui ; Zhang, Hongwei ; Yang, Yan

Author_Institution

Dept. of Mech. Eng., Univ. of Tianjin, Tianjin, China

fYear

2009

fDate

9-12 Aug. 2009

Firstpage

4458

Lastpage

4462

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.

Keywords

backpropagation; closed loop systems; marine control; neurocontrollers; radial basis function networks; self-adjusting systems; three-term control; traffic control; BP neural network; HTPE online motion law; PID control period; PID parameters rectification; RBF neural network; backpropagation neural network; deep sea hydrothermal plume explorer; depth parking control; motion forecasting model; radial basis function; self-adaptive PID control model; self-adjusting approach; Control systems; Lenses; Motion control; Neck; Neural networks; Oceans; Payloads; Predictive models; Sea measurements; Three-term control; BP neural network; Depth parking control; PID; RBF neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronics and Automation, 2009. ICMA 2009. International Conference on

Conference_Location

Changchun

Print_ISBN

978-1-4244-2692-8

Electronic_ISBN

978-1-4244-2693-5

Type

conf

DOI

10.1109/ICMA.2009.5244858

Filename

5244858