Compact format model-free adaptive course control algorithm of fusion neural network PD used for ship

A model-free self-adaptive, neural network technology, applied in attitude control, non-electric variable control, control/regulation system, etc., can solve the problems of ship heading system overshoot, oscillatory system, nonlinearity, etc., to improve the response speed , Reduce the effect of system overshoot and time-consuming adjustment

Active Publication Date: 2019-02-22
HARBIN ENG UNIV
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Problems solved by technology

[0004] The CFDL_MFAC algorithm belongs to the incremental integral structure. In addition, the ship heading system has large time delay, nonlinearity, and uncertainty. Applying the above improved model-free control algorithm directly to the ship heading control will cause integral saturation. phenomenon, the ship heading system produces serious overshoot, and the phenomenon of oscillation even makes the system unstable

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  • Compact format model-free adaptive course control algorithm of fusion neural network PD used for ship
  • Compact format model-free adaptive course control algorithm of fusion neural network PD used for ship
  • Compact format model-free adaptive course control algorithm of fusion neural network PD used for ship

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[0025] The present invention will be further described below in conjunction with accompanying drawing:

[0026] as attached figure 1 As shown, it is the overall block diagram of the heading system of the present invention. First, the command of the desired heading y(k)* is given, and e(k) is calculated according to the ship heading of the kth, k-1, and k-2 control cycles , Δe(k), Δ 2 e(k) is used as the input of the NN_PID_CFDL_MFAC controller, and the NN_PD_CFDL_MFAC controller solves the current expected input quantity u(k) of the ship heading system. The control mechanism executes the expected input command, updates the actual heading of the ship system, and sets k=k+1 to update e(k), Δe(k), Δ 2 e(k) is input to the NN_PD_CFDL_MFAC controller, and the above process is repeated, so that the actual heading of the ship converges to the desired heading.

[0027] as attached figure 2 Shown is the system flowchart of the present invention, specifically comprises the followin...

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Abstract

The invention belongs to the ship motion automatic control field and especially relates to the compact format model-free adaptive course control algorithm of a fusion neural network PD used for a ship. The algorithm comprises the following steps of based on the compact format model-free adaptive control algorithm, introducing a proportional item and a differential item to form a fusion PD type CFDL_MFAC algorithm; combining neural network control with a PD_CFDL_MFAC algorithm to provide the compact format model-free adaptive course control algorithm of fusion neural network PD; and calculatinga course deviation e(k), wherein the e(k)=y<*>(k)-y(k), and the like. Through introducing the proportional item and the differential item, the adaptability of the algorithm and the capability of resisting external uncertainty disturbance are increased.

Description

technical field [0001] The invention belongs to the field of automatic control of ship motion, and in particular relates to a compact format model-free self-adaptive course control algorithm of fusion neural network PD for ships. Background technique [0002] At present, in engineering applications, the control of ship heading basically adopts the PID control algorithm, but the ship is easily affected by model perturbation and environmental interference, which makes it difficult to maintain a consistent PID controller with a set of fixed parameters. control effect, the parameters need to be retuned to make the system stable. However, the controller developed based on the "model-oriented" design strategy relies heavily on the mathematical model of the system. Because it is very difficult to obtain an accurate mathematical model, there are unmodeled dynamics, model perturbations, etc., which lead to poor self-adaptation of the system, and it is difficult to obtain an accurate ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D1/08
CPCG05D1/0875
Inventor 廖煜雷杜廷朋姜权权姜文贾琪李晔成昌盛沈海龙张强
Owner HARBIN ENG UNIV
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