An Expert Experience Based Model Predictive Control Strategy for Dynamic Positioning

2015 ◽  
Author(s):  
Shi Lei ◽  
Han Bing ◽  
Cao Jianming
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Low Frequency ◽  
Good Control ◽  
Dynamic Positioning ◽  
Positioning System ◽  
Dynamic Positioning System ◽  
Technical Requirements ◽  
Control Accuracy

In this paper, an expert experience model predictive control strategy is suggested based on the feed forward control of wind and current, and the Kalman filter for the low frequency motion estimation. For the examination of control accuracy and control stability, a pool test of the dynamic positioning system is designed and completed based on the actual situation of the target ship in this paper. The test results with different simulated environments show that the control strategy of dynamic positioning system meets the technical requirements of the target ship with good control accuracy and stability.

Wave Filtering of Ship Dynamic Positioning System Using Particle Filter

2013 ◽  
Vol 397-400 ◽  
pp. 551-555
Author(s):  
Wen Juan Li ◽  
Hai Xiang Xu ◽  
Hui Feng
Keyword(s):  
Particle Filter ◽  
Low Frequency ◽  
Nonlinear Filter ◽  
Dynamic Positioning ◽  
Positioning System ◽  
Dynamic Positioning System ◽  
Frequency Position ◽  
Particle Filter Algorithm

This paper presents a nonlinear filter which is particle filter. The filter produces accurate estimates of low-frequency position and velocity only from measured values of ship position and heading in Dynamic Positioning System. The results of simulation confirm the validity and adaptability of the particle filter algorithm.

An Application of Self-Tuning Fuzzy Controller to Dynamic Positioning System of Floating Production System

1996 ◽  
Vol 118 (4) ◽  
pp. 241-246 ◽  
Author(s):  
Y. Inoue ◽  
J. Du
Keyword(s):  
Fuzzy Control ◽  
Control Strategy ◽  
Production System ◽  
Deep Sea ◽  
Floating Body ◽  
Dynamic Positioning ◽  
Positioning System ◽  
Mooring System ◽  
Dynamic Positioning System ◽  
Self Tuning

Position-keeping of a floating body is a very important matter in a production system in the deep sea. How a floating body can be kept stationary is a key problem in this study. Conventionally, the mooring system is adopted to position a floating body under disturbance of wind, wave, and current; but, in general, it is difficult to survive storm disturbance in the deep sea. The dynamic positioning system can solve this problem, but thrusters must be operated at all times against random drift force of wave, wind, and current, and a great deal of energy is required. So a composite system of single-point mooring system and fuzzy control dynamic positioning (FDP) system was studied in a previous paper (Inoue, 1994). However, up to now in almost all kinds of dynamic positioning systems, control strategy must be decided in advance. This is unreasonable under a random condition. In this paper, a self-tuning fuzzy control system is put forward. By this approach, the control precision of the previous fuzzy dynamic positioning is fairly improved. The main concept of this approach is that at first a feasible control strategy is decided in advance, then during the motion of the system, the control strategy is improved automatically according to the sea conditions of operation. This system is more reasonable than the previous system because, in fact, the disturbance of wave, current, and wind cannot be predicted.

scholarly journals A Control Method for IPMSM Based on Active Disturbance Rejection Control and Model Predictive Control

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 760
Author(s):  
Fang Liu ◽  
Haotian Li ◽  
Ling Liu ◽  
Runmin Zou ◽  
Kangzhi Liu
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Duty Cycle ◽  
Control Strategy ◽  
Disturbance Rejection ◽  
Torque Ripple ◽  
Current Control ◽  
Active Disturbance Rejection Control ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control

In this paper, the speed tracking problem of the interior permanent magnet synchronous motor (IPMSM) of an electric vehicle is studied. A cascade speed control strategy based on active disturbance rejection control (ADRC) and a current control strategy based on improved duty cycle finite control set model predictive control (FCSMPC) are proposed, both of which can reduce torque ripple and current ripple as well as the computational burden. First of all, in the linearization process, some nonlinear terms are added into the control signal for voltage compensation, which can reduce the order of the prediction model. Then, the dq-axis currents are selected by maximum torque per ampere (MTPA). Six virtual vectors are employed to FCSMPC, and a novel way to calculate the duty cycle is adopted. Finally, the simulation results show the validity and superiority of the proposed method.

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