Self-adaption filtering method tracking environmental force sudden change in dynamic positioning

Abstract

The invention relates to a self-adaption filtering method tracking environmental forces sudden change in dynamic positioning. The method includes steps of establishing a state space model of a ship system; using traceless Kalman filtering for estimating a state in the current moment; judging whether the environmental force in the current moment meets sudden change or not according to a judgment condition; performing self-adaption adjustment on posterior mean square error of the current moment if the judgment condition is met, which means environmental force sudden change occurs; judging positioning is completed or not; if the positioning is not finished, sending a thrust command of the next moment by a control system according to a position and the environmental force estimated by the self-adaption filtering so as to enable a ship to reach a positioning point and to hold at the positioning point; if the positioning is finished, finishing the whole cycle. According to the invention, environmental force sudden change can be judged and estimated value convergence to a real value can be realized quickly by modifying a posterior mean square error matrix, so that the controller can generate a thrust for compensating the environmental force and deviation between a real position and a set position can be reduced.

Description

technical field [0001] The invention relates to ship dynamic positioning, in particular to an adaptive filtering method for tracking environmental force mutations in dynamic positioning. Background technique [0002] A ship equipped with a dynamic positioning system uses the thrust from the propulsion system to maintain its own position, has good maneuverability, can quickly enter or leave the working mode, and has high safety. The main function of filtering and state estimation in the dynamic positioning control system is to filter out the high-frequency noise of the measured value, separate the measured value into low-frequency and high-frequency parts, and make the propulsion system only offset slowly changing low-frequency external disturbances as much as possible. In addition, filtering can also estimate parameters such as speed and environmental force that cannot be given by the measurement system. [0003] At present, the most commonly used filtering methods are Kalm...

AI Technical Summary

Problems solved by technology

[0004] Although the filtering algorithm can estimate parameters such as position, velocity, and environmental force, the ability to track sudden changes in state values ​​is poor.

If the environmental force changes suddenly, the filter needs a long period of time to estimate the sudden environmental force, resulting in a large deviation in the position of the ship

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