Composite orthogonal neural network prediction control-based intelligent ship tracking method

Abstract

The invention discloses a composite orthogonal neural network prediction control-based intelligent ship tracking method. The method comprises the steps of: obtaining a predetermined trajectory in themovement process of a ship, and calculating optimization algorithm predicted thrust of each propeller through an optimization algorithm according to the predetermined trajectory and predicted outputs;carrying out weighted stacking on the optimization algorithm predicted thrust and neural network predicted thrust through the neural network predicted thrust so as to output thrust to be generated byeach propeller; predicting the position, heading and speed of the ship through a prediction model; and correcting predicted values of the position, heading and speed of the ship and taking the corrected predicted values as the predicted outputs. According to the method, a composite orthogonal neural network is combined to put forward a new model prediction strategy; the neural network is simple in algorithm and high in learning convergence speed, and has the excellent characteristics of high linear and nonlinear approximation accuracy and the like; and the learning algorithm of the neural network can be offline completed, so that the online calculation time is greatly shortened.

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...

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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

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