Linear dynamics system motion state prediction method and system

Abstract

The invention provides a linear dynamic system motion state prediction method and system, and the method comprises the steps: carrying out the Kalman filtering of an obtained state measurement value,and obtaining a filtered state quantity; predicting a state transition matrix by using the filtered state quantity to obtain a calibrated and updated state transition matrix; and predicting the motionstate of the linear dynamics system by using the filtered state quantity and the calibrated and updated state transition matrix. According to the method, the required observed quantity is extremely small, the calculated quantity can be reduced, and compared with other methods, the method has the advantages of high adaptability and high accuracy.

Description

technical field [0001] The invention relates to the technical field of linear dynamic system control, in particular to a method and system for predicting the motion state of a linear dynamic system. Background technique [0002] Dynamical system state prediction, such as the prediction of the ship's attitude motion during navigation activities, can be obtained by multiplying the immediate state by several powers of the discretized system state transition matrix when the ship dynamics mathematical model is known The predicted value of the ship state after several steps. However, in most cases, the designer of the ship cannot know the accurate dynamic mathematical model for some reasons such as confidentiality or inaccurate measurement. To solve this problem, the existing method is to carry out system identification on the mathematical model of ship system dynamics, and continuously improve the accuracy of the dynamic model through a large amount of prior data, but this means...

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Problems solved by technology

However, in most cases, the designer of the ship cannot know the accurate dynamic mathematical model due to reasons such as confidentiality or inaccurate measurement.

To solve this problem, the existing method is to carry out system identification on the mathematical model of ship system dynamics, and continuously improve the accuracy of the dynamic model through a large amount of prior data, but this means that the amount of calculation will continue to increase with the increase of prior data. A large amount of prior data mixed with random motion sometimes makes the identification accuracy worse, resulting in lower accuracy of identification results

In view of the seemingly periodic motion of the ship attitude, some scholars have proposed that Fourier transform can be used to analyze its motion mode, and a large amount of data can be used for training through machine learning, neural network and other methods. Most of these methods are based on statistical related theories. The implementation process is more complicated

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