An Intelligent Ship Tracking Method Based on Composite Orthogonal Neural Network Predictive Control

Abstract

The invention discloses an intelligent ship tracking method based on composite orthogonal neural network predictive control, comprising the steps of: obtaining a predetermined trajectory during the movement of the ship, and calculating the predetermined trajectory and the predicted output through an optimization algorithm to calculate the position of each propeller The optimization algorithm predicts the thrust; predicts the thrust through the neural network, and outputs the thrust that each thruster should produce by weighting the optimization algorithm predicted thrust and the neural network predicted thrust; predicts the position, heading, and speed of the ship through the prediction model; Correct the predicted values ​​of position, heading, and speed, and use the corrected predicted values ​​as the aforementioned predicted output. The present invention combines a compound orthogonal neural network to propose a new model prediction strategy. The neural network algorithm is simple, the learning convergence speed is fast, and it has excellent characteristics such as linear and nonlinear approximation precision, and the learning algorithm of the neural network can be completed offline. The online computing time is greatly reduced.

Description

technical field [0001] The invention relates to the technical field of ship intelligent control, in particular to an intelligent ship tracking method based on composite orthogonal neural network predictive control. Background technique [0002] In recent years, the development of information, computer, communication, network, new energy, artificial intelligence and other technologies, as well as the application of Internet of Things, big data, integrated ship bridge system and cyber physical system, have greatly promoted the process of ship intelligence. Smart ships include many tasks, and how to use environmental perception information to realize autonomous tracking control of smart ships is one of the important contents. [0003] As more and more ships or platforms operating in deep water will be equipped with autonomous tracking systems, the marine industry now has higher and higher requirements for the safety and reliability of smart ships or platforms, but the ships the...

AI Technical Summary

Problems solved by technology

[0003] As more and more ships or platforms operating in deep water will be equipped with autonomous tracking systems, the marine industry now has higher and higher requirements for the safety and reliability of smart ships or platforms, but the ships themselves are nonlinear, and The complex and changeable marine environment in Shanghai makes the autonomous tracking control of intelligent ships a nonlinear, complex and time-varying control problem

Although the use of model prediction can solve these complex nonlinear problems, there is an obvious problem that the optimization calculation is too large, and even the system cannot give control within the specified control time. Therefore, we combined the composite orthogonal neural network Propose a new model prediction strategy

Images