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.
If the inline PDF is not rendering correctly, you can download the PDF file here.
1. Angelo C Bossio G Garcia G et al.2006. Speed control of PMSMs with interconnection an damping assignment or feedback linearization comments about their performance. Industrial Electronics. Montreal 2182-2187.
2. Cetin E Oguz U2008. A hybrid controller for the speed of a permanent magnet synchronous motor drive [J]. Control Engineering Practice16 260-270.
3. Islam M F Veitch B Liu P et al. 2010. Gap Effect on Performance of Podded Propulsors in Straight-Ahead and Azimuthing Conditions. Marine Technology 4747-58.
4. Ji FengFu LijunYe Zhihao2011. Study on Vector Control for Vessel Electric Propulsion. Journal of wuhan university of science and technology35361-364
5. LI Bingqiang LIN Hui2011. Direct Control of Current Vector for Surface-mounted Permanent Magnet Synchronous Motor. Proceedings of the CSEE31288-294
6. LI Liang-liang HE YongYE Hai-xiang 2011. Simulation of Permanent Magnet Synchronous Motor Vector Control Based on ITAE Optimization. Electric Machines & Control Application38 31-45
7. Li ZhongbingZhang Huanren2011. Extended Kalman Filter Enhanced Ship Electrical Propulsion System Navigation of China 34 45-50
8. Lu Wenbin Yao Wenxi Lu Zhengyu2013. Speed Sensorless Vector Control with Improved Closed-Loop Flux Observer for Induction Machines. Transactions of China Electro Technical Society28 148-153.
9. R.V.MerweA.DouvetN.De.Freitas et al2000. The unscented particle filter.Technical Report CUED/F@ INPENG/ TR380:Cambridge University Engi -neering Department.
10. Sui Shu-linYao Wen-long2008. Spacecraft of autonomous optical navigation Based on wavelet-UPF. Systems Engineering and Elctronics. 81519-1522
11. Suman Maiti Chandan Chakraborty Sabyasachi Sengupta 2009. Simulation studies on model reference adaptive controller based speed estimation technique for the vector controlled permanent magnet synchronous motor drive. Simulation Modelling Practice and Theory7 585-596.
12. Van Dyke M C Schwartz J L Hall C D2004. Unscented Kalman filtering for spacecraft attitude state and parameter estimation. http: www.Space-flightorg/AAS_meetings/2004_winter/w2004-program.pdf 2004-03-10.
13. WANG Li-peng ZHANG Hua-guangLIU Xiuchong 2012. Integral backstepping controller in the sensorless vector-control system for permanent magnet synchronous motor. Control Theory & Applications 29199-204
14. Wenlong Yao Yuan Liu Jundong Zhang Sun Ming Zhang Gui-chen and Wei shao2013. Design of Vector Control based on MFAC for SSP Podded Propulsion Proc. of the Int. Conf. on Robotics and Biomimetics (ROBIO 2013) 122418-2423
15. Wenlong Yao Jundong ZhangRonghu Chi Zhang Gui-chen. Model-free adaptive vector control of ship podded SSP propulsion motor Journal of Traffic and Transportation Engineering 201414(6): 59-66.
16. YU Ming CONG Shuang XU Juan2008. Design of nonlinear motor adaptive fuzzy sliding mode controller based on GA Journal of System Simulation. 20 3141-3145.
17. ZHANG Guichen MA Jie 2010. Study and Application of Podded Electric Propulsion System Based on SIMOTION. Ship Building of China 5145-50.