Optimal Control Strategy for Marine Ssp Podded Propulsion Motor Based on Strong Tracking-Epf

Aiming at the non-linearity of state equation and observation equation of SSP (Siemen Schottel Propulsor) propulsion motor, an improved particle filter algorithm based on strong tracking extent Kalman filter (ST-EKF) was presented, and it was imported into the marine SSP propulsion motor control sys...

Full description

Bibliographic Details
Main Authors: Yao Wenlong, Liu Yuan, Sun Honghai, Zhang Guichen, Zhang Jundong, Zhou Mingshun, Sun Ming, Jiang Dezhi
Format: Article
Language:English
Published: Sciendo 2015-09-01
Series:Polish Maritime Research
Subjects:
epf
Online Access:https://doi.org/10.1515/pomr-2015-0024
id doaj-2a67d3079dbd4614b14ff77aedcb31da
record_format Article
spelling doaj-2a67d3079dbd4614b14ff77aedcb31da2021-09-05T13:59:48ZengSciendoPolish Maritime Research2083-74292015-09-0122s14910.1515/pomr-2015-0024pomr-2015-0024Optimal Control Strategy for Marine Ssp Podded Propulsion Motor Based on Strong Tracking-EpfYao Wenlong0Liu Yuan1Sun Honghai2Zhang Guichen3Zhang Jundong4Zhou Mingshun5Sun Ming6Jiang Dezhi7Dept. of Marine Engineering, Qingdao Ocean Shipping Mariners College, Qingdao, ChinaDept. of Naval Architecture & Ocean Engineering, Qingdao OceanShipping Mariners College, Qingdao, ChinaDept. of Naval Architecture & Ocean Engineering, Qingdao OceanShipping Mariners College, Qingdao, ChinaSchool of Shipbuilding Engineering, Journal of Harbin Engineering University, Harbin, ChinaCollege of Marine Engineering, Dalian Maritime University, Dalian, ChinaDept. of Marine Engineering, Qingdao Ocean Shipping Mariners College, Qingdao, ChinaDept. of Marine Engineering, Qingdao Ocean Shipping Mariners College, Qingdao, ChinaDept. of Marine Engineering, Qingdao Ocean Shipping Mariners College, Qingdao, ChinaAiming at the non-linearity of state equation and observation equation of SSP (Siemen Schottel Propulsor) propulsion motor, an improved particle filter algorithm based on strong tracking extent Kalman filter (ST-EKF) was presented, and it was imported into the marine SSP propulsion motor control system. The strong tracking filter was used to update particles in the new algorithm and produce importance densities. As a result, the problems of particle degeneracy and sample impoverishment were ameliorated, the propulsion motor states and the rotor resistance were estimated simultaneously using strong track filter (STF), and the tracking ability of marine SSP propulsion motor control system was improved. Simulation result shown that the improved EPF algorithm was not only improving the prediction accuracy of the motor states and the rotor resistance, but also it can satisfy the requirement of navigation in harbor. It had the better accuracy than EPF algorithm.https://doi.org/10.1515/pomr-2015-0024marine electric propulsionepfst-epfssp podded propulsionpropulsion motor
collection DOAJ
language English
format Article
sources DOAJ
author Yao Wenlong
Liu Yuan
Sun Honghai
Zhang Guichen
Zhang Jundong
Zhou Mingshun
Sun Ming
Jiang Dezhi
spellingShingle Yao Wenlong
Liu Yuan
Sun Honghai
Zhang Guichen
Zhang Jundong
Zhou Mingshun
Sun Ming
Jiang Dezhi
Optimal Control Strategy for Marine Ssp Podded Propulsion Motor Based on Strong Tracking-Epf
Polish Maritime Research
marine electric propulsion
epf
st-epf
ssp podded propulsion
propulsion motor
author_facet Yao Wenlong
Liu Yuan
Sun Honghai
Zhang Guichen
Zhang Jundong
Zhou Mingshun
Sun Ming
Jiang Dezhi
author_sort Yao Wenlong
title Optimal Control Strategy for Marine Ssp Podded Propulsion Motor Based on Strong Tracking-Epf
title_short Optimal Control Strategy for Marine Ssp Podded Propulsion Motor Based on Strong Tracking-Epf
title_full Optimal Control Strategy for Marine Ssp Podded Propulsion Motor Based on Strong Tracking-Epf
title_fullStr Optimal Control Strategy for Marine Ssp Podded Propulsion Motor Based on Strong Tracking-Epf
title_full_unstemmed Optimal Control Strategy for Marine Ssp Podded Propulsion Motor Based on Strong Tracking-Epf
title_sort optimal control strategy for marine ssp podded propulsion motor based on strong tracking-epf
publisher Sciendo
series Polish Maritime Research
issn 2083-7429
publishDate 2015-09-01
description Aiming at the non-linearity of state equation and observation equation of SSP (Siemen Schottel Propulsor) propulsion motor, an improved particle filter algorithm based on strong tracking extent Kalman filter (ST-EKF) was presented, and it was imported into the marine SSP propulsion motor control system. The strong tracking filter was used to update particles in the new algorithm and produce importance densities. As a result, the problems of particle degeneracy and sample impoverishment were ameliorated, the propulsion motor states and the rotor resistance were estimated simultaneously using strong track filter (STF), and the tracking ability of marine SSP propulsion motor control system was improved. Simulation result shown that the improved EPF algorithm was not only improving the prediction accuracy of the motor states and the rotor resistance, but also it can satisfy the requirement of navigation in harbor. It had the better accuracy than EPF algorithm.
topic marine electric propulsion
epf
st-epf
ssp podded propulsion
propulsion motor
url https://doi.org/10.1515/pomr-2015-0024
work_keys_str_mv AT yaowenlong optimalcontrolstrategyformarinessppoddedpropulsionmotorbasedonstrongtrackingepf
AT liuyuan optimalcontrolstrategyformarinessppoddedpropulsionmotorbasedonstrongtrackingepf
AT sunhonghai optimalcontrolstrategyformarinessppoddedpropulsionmotorbasedonstrongtrackingepf
AT zhangguichen optimalcontrolstrategyformarinessppoddedpropulsionmotorbasedonstrongtrackingepf
AT zhangjundong optimalcontrolstrategyformarinessppoddedpropulsionmotorbasedonstrongtrackingepf
AT zhoumingshun optimalcontrolstrategyformarinessppoddedpropulsionmotorbasedonstrongtrackingepf
AT sunming optimalcontrolstrategyformarinessppoddedpropulsionmotorbasedonstrongtrackingepf
AT jiangdezhi optimalcontrolstrategyformarinessppoddedpropulsionmotorbasedonstrongtrackingepf
_version_ 1717812997419696128