USV course control by ant colony optimization PID with mutation characteristics

YUAN Guangming; FENG Guoqiang; DING Chengjun; FENG Yubo

doi:10.13873/J.1000-9787(2020)08-0016-04

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USV course control by ant colony optimization PID with mutation characteristics

更新时间：2025-09-11

- USV course control by ant colony optimization PID with mutation characteristics
- Issue 8, Pages: 16-19(2020)
- 作者机构：
  
  河北工业大学机械工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2020)08-0016-04
  CLC： TP18;U664.82
- Published：2020
- 稿件说明：
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YUAN Guangming, FENG Guoqiang, DING Chengjun, et al. USV course control by ant colony optimization PID with mutation characteristics[J]. 2020, (8): 16-19.
DOI：

YUAN Guangming, FENG Guoqiang, DING Chengjun, et al. USV course control by ant colony optimization PID with mutation characteristics[J]. 2020, (8): 16-19. DOI： 10.13873/J.1000-9787(2020)08-0016-04.

摘要

针对水面无人船(USV)航向控制存在的实时性差、精度不高等缺陷

提出一种利用具有变异特征的蚁群算法优化比例—积分—微分(PID)参数来进行USV航向控制的方法。在USV航向控制运动学模型的基础上

以绝对误差的矩的积分作为目标函数

将改进的适用于PID参数优化的变异算子引入到蚁群算法中

对USV航向控制过程进行PID参数优化。并对比具有变异特征的蚁群优化PID航向控制算法、常规蚁群PID航向控制算法及PID航向控制算法对USV航向控制的控制效果。仿真实验及实地测试表明:所提方法在参数优化过程中比常规蚁群算法收敛速度快

寻优能力强

具有航向控制超调量小、跟踪速度快以及控制稳定等优点。

Abstract

Aiming at the shortcomings such as poor real-time performance and low precision of the current water surface unmanned course control

a new method of optimizing proportional-integral-derivative（ PID） parameters by using ant colony algorithm with mutation features to carry out unmanned surface vehicle（ USV） course control is proposed. On the basis of USV course control kinematics model

the integral of the moment of absolute error is taken as the objective function

and the improved mutation operator suitable for PID parameter optimization is introduced into the ant colony algorithm to optimize the PID parameters of the USV course control process. The control effects of ant colony optimization PID course control algorithm

conventional ant colony PID course control algorithm and PID course control algorithm on USV course control are compared. The simulation experiment and field test show that the control method has faster convergence speed and better optimizing ability than the conventional ant colony algorithm in the parameter optimization process

and has the advantages of small heading control overshoot

fast tracking speed and stable control.

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