Application of Kalman filter in Three Beidou Geostationary Satellites passive dynamic positioning

2008 ◽  
Author(s):  
Wu Xiao-dong ◽  
Wu Si-liang ◽  
Wang Ju ◽  
Li Jia-qi
Keyword(s):  
Kalman Filter ◽  
Dynamic Positioning ◽  
Geostationary Satellites

DP-Stability During Heavy Lift Operations Using a Modified Kalman Filter

2018 ◽  
Author(s):  
Eelco Harmsen ◽  
Radboud van Dijk ◽  
Petter Stuberg
Keyword(s):  
Kalman Filter ◽  
Dynamic Positioning ◽  
Mooring System ◽  
Engineering Analysis ◽  
Single Object ◽  
Joint Effort ◽  
Wide Range ◽  
Floating Object ◽  
Heavy Lift ◽  
Modified Kalman Filter

During heavy lift operations, staying on position using a Dynamic Positioning (DP) system offers many advantages compared with a mooring system. However, when the vessel is connected to another fixed or floating object during the lifting operation through its hoist wires it may experience instabilities in the DP-system. These DP-instabilities are caused by the inability of the DP system to handle the relatively stiff external spring of the hoist wire correctly. This phenomenon is well known and mitigating measures such as Heavy Lift Mode have been developed over the years that work well for stationary vessels. However, when two vessels are lifting a single object together (e.g. QUAD lift), existing solutions to prevent this DP-instability are insufficient, as the nature of such lift requires a synchronous move on DP. During studies to the fundamental behavior of a DP system during heavy lift operations it is found that modifications to the Kalman filter can prevent these DP-instabilities. Heerema Marine Contractors presented the DP-stability challenges to Kongsberg Maritime, and a joint effort resulted in an implementation of a modified Kalman filter in the Kongsberg Maritime DP system. Also a dedicated engineering analysis to predict risk of DP-instabilities for specific lift configurations has been developed. The modified DP-system is tested in large number of simulations (both desktop and a full mission simulator) to test the ability of the updated DP-system to deal with a wide range of specific heavy lift conditions. Results were evaluated between Heerema office, Kongsberg and offshore personnel for developing the optimum Kalman filter parameters. Finally, the system is tested during a dedicated DP-trial program onboard Thialf. As the results of all these tests were very successful, the new High Kalman filter was made available onboard Thialf as a permanent option next to the original functionalities. The paper addresses the steps followed to define the new Kalman filter settings, the simulations performed to test the new filter as well as to show results of the offshore tests that were done to validate the numerical analysis.

Dynamic Positioning Observer Design Using Exogenous Kalman Filter

2019 ◽  
Author(s):  
Song An ◽  
Dengshuo Chen ◽  
Yong Bai
Keyword(s):  
Kalman Filter ◽  
Stability Property ◽  
Measurement Data ◽  
Observer Design ◽  
Nonlinear Observer ◽  
Dynamic Positioning ◽  
Model Uncertainties ◽  
Process Noise ◽  
Second Stage ◽  
The Stability

Abstract Observer of a dynamic positioning (DP) system utilizes DP’s measurement data, to predict vessel’s velocities, positions, and unknown environmental forces on the vessel, as well as to handle model uncertainties and errors. Stability and optimality of a DP observer is important for overall DP performance. Nonlinear observer (NLO) designs usually take global asymptotic or exponential stability as the primary goal, and discard the noise. On the other hand, linearized Kalman filter (KF) algorithm, e.g. the extended Kalman filter (EKF), is optimal in minimizing the covariance of observer states by taking both measurement and process noise into account. The applied exogenous Kalman filter (XKF) algorithm in this paper, is a two-stage cascade of NLO and linearized KF, which uses the first-stage NLO estimated states as exogenous inputs for the second-stage linearized KF. XKF approach is proved to have both the stability property inherited from NLO and optimality from linearized KF. Stability and optimality of XKF based observer is studied through DP station-keeping numerical simulations.

Kalman Filter for Dynamic Positioning Ships Using Position and Acceleration Feedback

2014 ◽  
Vol 543-547 ◽  
pp. 2362-2367
Author(s):  
Ye Hai Xie
Keyword(s):  
Computer Simulation ◽  
Kalman Filter ◽  
Stability Theory ◽  
Linear Acceleration ◽  
Dynamic Positioning ◽  
Positioning Systems ◽  
Measured Position ◽  
Acceleration Feedback ◽  
The Stability

Considering the problem of dynamic positioning Systems for the slowly-varying disturbances, a new kalman filter using position and acceleration feedback is presented. The kalman filiter separates the WF motions from the measured position and linear acceleration, and estimates the LF position, velocity and acceleration. The stability of this filiter is proven by applying input-to-state (ISS) stability theory. Finally, the computer simulation is given to demonstrate the effectiveness of the proposed method.

Critical Analysis of Control and Filtering Algorithms Used in Real Dynamic Positioning Systems

2005 ◽  
Author(s):  
Eduardo A. Tannuri ◽  
Helio M. Morishita ◽  
Vinicius L. M. Veras ◽  
Glenan A. Lago
Keyword(s):  
Kalman Filter ◽  
Critical Analysis ◽  
Wind Waves ◽  
Mathematical Formulation ◽  
Estimation Algorithm ◽  
Peak Frequency ◽  
Floating Body ◽  
Dynamic Positioning ◽  
Sea Waves ◽  
Positioning Systems

Dynamic positioning systems (DPS) comprise the utilization of active propulsion to maintain the position and heading of a vessel. Sensors are used to measure the actual position of the floating body, and a control algorithm is responsible for the calculation of forces to be applied to each propeller, in order to counteract all environmental forces, including wind, waves and current loads. The controller cannot directly compensate motions in the sea waves frequency range, since they would require an enormous power to be attenuated, possibly causing damage to the propeller system. A filtering algorithm is then used to separate high frequency components from the low frequency ones, which are indeed controlled. Usual commercial systems apply Kalman filtering technique to perform such task, which includes a full model of the system. Furthermore, an adaptive on-line estimation algorithm is also used to evaluate the wave peak frequency, since the model in Kalman Filter depends on such parameter. The controller itself is based on a simple proportional-derivative (PD) actions. This paper presents all the mathematical formulation of the Kalman Filter, adaptive algorithm and the controller used in commercial DPS and performs a critical analysis of those models. Some illustrative results of a dynamic positioned shuttle vessel are presented, considering the incidence of waves, current and winds.

Sign in / Sign up

Export Citation Format

Share Document