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A compact model-free adaptive heading control algorithm based on fused neural network pd for ships

A model-free self-adaptive, heading control technology, applied in attitude control, control/regulation system, non-electric variable control and other directions, can solve the problems of ship heading system overshoot, oscillating system, nonlinearity, etc., to improve resistance to the outside world. Uncertain interference, improve response speed, reduce the effect of time-consuming adjustment

Active Publication Date: 2022-03-18
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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  • A compact model-free adaptive heading control algorithm based on fused neural network pd for ships
  • A compact model-free adaptive heading control algorithm based on fused neural network pd for ships
  • A compact model-free adaptive heading control algorithm based on fused neural network pd for ships

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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 field of automatic control of ship motion, and in particular relates to a compact model-free adaptive course control algorithm of ship fusion neural network PD; Items constitute a fusion PD-type CFDL_MFAC algorithm; combine the neural network control with the PD_CFDL_MFAC algorithm to propose a compact format model-free adaptive heading control algorithm for fusion neural network PD; calculate the heading deviation e(k), where e(k)=y * (k)‑y(k) etc. The invention improves the adaptability of the algorithm and the ability to resist external uncertain interference through the introduction of the proportional item and the differential item.

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 Patents(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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