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Ship course intelligence controller based on incomplete recursive branch-set dynamic neural network

A dynamic neural network, intelligent controller technology, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve problems such as lack of effective control, and achieve improved control effect, small steering amount, and ship heading switching. Control process for fast effects

Inactive Publication Date: 2016-03-30
CHANGAN UNIV
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Problems solved by technology

From the basic principle of fuzzy control, it can be seen that the deviation change quantitatively reflects the change direction and size of the state quantity of the controlled process at the current moment. Therefore, fuzzy control has a certain predictive ability for pure lag systems, but for large lag systems ( τ s / T m >0.5) there is no effective control

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  • Ship course intelligence controller based on incomplete recursive branch-set dynamic neural network
  • Ship course intelligence controller based on incomplete recursive branch-set dynamic neural network
  • Ship course intelligence controller based on incomplete recursive branch-set dynamic neural network

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Embodiment Construction

[0035] The invention discloses an intelligent ship heading controller based on an incomplete recursive support dynamic neural network, which includes sequentially connected input conversion devices, steering gear controllers, and neural network controllers, such as figure 1 shown; the input of the entire intelligent controller includes the actual heading angle y of the ship at time τ p (τ), the external disturbance d(τ) of sea wind and waves, and the expected course angle y d ; The output is the control quantity u(τ) of the ship.

[0036] An intelligent ship heading controller based on incomplete recursive support dynamic neural network, including sequentially connected input conversion device, steering gear controller, and neural network controller;

[0037] In the neural network controller, an incomplete recursive support dynamic neural network is established, and the construction method of the network includes:

[0038] On the basis of the local regression neural network,...

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Abstract

The invention discloses a ship course intelligence controller based on an incomplete recursive branch-set dynamic neural network. The controller comprises an input conversion device, a steering engine controller and a neural network controller which are successively connected. The incomplete recursive branch-set dynamic neural network is established in the neural network controller and a ship course prediction control strategy is given out. By using the controller of the invention, a problem that a traditional steering engine controller can not control a large lagged ship accurately is solved; and a ship course switching control process is rapid and smooth and a steering amount is small so that high-precision ship course control is realized.

Description

technical field [0001] The invention relates to the field of computer intelligent ship motion control, in particular to an intelligent ship heading controller based on an incomplete recursive support dynamic neural network model. Background technique [0002] Ship motion is a complex control problem, which is a dynamic process with large inertia, large time delay, nonlinearity and time-varying parameters. For example, the time constant of a 10,000-ton oil tanker can reach more than 100s, and the response to the rudder is slow. Some open-loop unstable ships even have an abnormal response to the rudder (turning the right rudder bow within a certain rudder angle limit turns left instead. ), its control is quite difficult. At the same time, the motion characteristics of the ship change with the speed, load, trim, water depth and other factors, and the disturbance characteristics also vary with the sea conditions such as waves, winds, and currents. Therefore, the ship motion con...

Claims

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Application Information

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IPC IPC(8): G05B13/04
Inventor 吕进吕永红孙广成吕若琳
Owner CHANGAN UNIV
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