research-article

Design of Ship Engine Speed System Based on RBF Neural Network PID Control

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Li GengAuthors Info & Claims

EITCE '21: Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering

Pages 179 - 183

https://doi.org/10.1145/3501409.3501442

Published: 31 December 2021 Publication History

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Abstract

The speed control of ship sailing on the water surface has become one of the important research contents of ship sailing stability. The traditional PID control model is not suitable for the stable navigation of ships, and there are some shortcomings such as too long adjustment time, too much overshoot and long rise time of the control system. In order to improve the control precision of ship engine speed system, design of Ship Engine Speed System Based on RBF Neural Network PID Control is proposed. The RBF neural network model is used to obtain a certain adaptive ability, and the gradient descent algorithm is used to obtain the optimal control parameters of the ship engine speed, so that the PID control effect reaches the optimal. The experimental simulation results show that compared with the traditional PID control, the RBF neural network PID control system overshoot is very small, the adjustment time and rise time is relatively short, and the control effect is very well.

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Liu L(2023)Research on Optimization Design of RBF Neural Network Based on Ant Colony Algorithm2023 2nd International Conference on 3D Immersion, Interaction and Multi-sensory Experiences (ICDIIME)10.1109/ICDIIME59043.2023.00008(11-15)Online publication date: Jun-2023
https://doi.org/10.1109/ICDIIME59043.2023.00008
Zhang ZLiu RZhou GDong SLiu ZXia XLiu GDing H(2022)Research on nonlinear model predictive control of regulated two-stage turbocharging system of diesel engine at high altitudesEnergy Sources, Part A: Recovery, Utilization, and Environmental Effects10.1080/15567036.2022.212512144:4(8718-8735)Online publication date: 20-Sep-2022
https://doi.org/10.1080/15567036.2022.2125121

Index Terms

Design of Ship Engine Speed System Based on RBF Neural Network PID Control
1. Networks
  1. Network algorithms
    1. Control path algorithms
      1. Network control algorithms
      2. Network design and planning algorithms

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EITCE '21: Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering

October 2021

1723 pages

ISBN:9781450384322

DOI:10.1145/3501409

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 31 December 2021

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EITCE 2021

EITCE 2021: 2021 5th International Conference on Electronic Information Technology and Computer Engineering

October 22 - 24, 2021

Xiamen, China

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EITCE '21 Paper Acceptance Rate 294 of 531 submissions, 55%;

Overall Acceptance Rate 508 of 972 submissions, 52%

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Liu L(2023)Research on Optimization Design of RBF Neural Network Based on Ant Colony Algorithm2023 2nd International Conference on 3D Immersion, Interaction and Multi-sensory Experiences (ICDIIME)10.1109/ICDIIME59043.2023.00008(11-15)Online publication date: Jun-2023
https://doi.org/10.1109/ICDIIME59043.2023.00008
Zhang ZLiu RZhou GDong SLiu ZXia XLiu GDing H(2022)Research on nonlinear model predictive control of regulated two-stage turbocharging system of diesel engine at high altitudesEnergy Sources, Part A: Recovery, Utilization, and Environmental Effects10.1080/15567036.2022.212512144:4(8718-8735)Online publication date: 20-Sep-2022
https://doi.org/10.1080/15567036.2022.2125121

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Backstepping Control of PMSM Based on RBF Neural Network

Inverted Pendulum RBF Neural Network PID Controller Design

Application of RBF Neural Network PID Controller in the Rectification Column Temperature Control System

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Backstepping Control of PMSM Based on RBF Neural Network

Inverted Pendulum RBF Neural Network PID Controller Design

Application of RBF Neural Network PID Controller in the Rectification Column Temperature Control System

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