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基于粒子群优化的波浪补偿控制系统研究

Control system research in wave compensation based on particle swarm optimization.

作者信息

Tang Gang, Lu Peng, Hu Xiong, Men Shaoyang

机构信息

School of Logistics Engineering, Shanghai Maritime University, Shanghai, 201306, China.

School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, 510006, China.

出版信息

Sci Rep. 2021 Jul 28;11(1):15316. doi: 10.1038/s41598-021-93973-4.

Abstract

For the offshore wave compensation control system, its controller setting will directly affect the platform's compensation effect. In order to study the wave compensation control system and optimization strategy, we build and simulate the wave compensation control model by using particle swarm optimization (PSO) to optimize the controller's control parameters and compare the results with other intelligent algorithms. Then we compare the response errors of the wave compensation platform under different PID controllers; and compare the particle swarm algorithm's response results and the genetic algorithm to the system controller optimization. The results show that the particle swarm algorithm is 63.94% lower than the genetic algorithm overshoot, and the peak time is 0.26 s lower, the adjustment time is 1.4 s lower than the genetic algorithm. It shows that the control effect of the wave compensation control system has a great relationship with the controller's parameter selection. Meanwhile, the particle swarm optimization algorithm's optimization can set the wave compensation PID control system, and it has the optimization effect of small overshoot and fast response time. This paper proposes the application of the particle swarm algorithm to the wave compensation system. It verifies the superiority of the method after application, and provides a new research reference for the subsequent research on the wave compensation control systems.

摘要

对于海上波浪补偿控制系统,其控制器设置将直接影响平台的补偿效果。为了研究波浪补偿控制系统及优化策略,我们通过使用粒子群优化算法(PSO)对控制器的控制参数进行优化,构建并仿真波浪补偿控制模型,并将结果与其他智能算法进行比较。然后我们比较了不同PID控制器下波浪补偿平台的响应误差;并将粒子群算法的响应结果与遗传算法对系统控制器优化的结果进行比较。结果表明,粒子群算法的超调量比遗传算法低63.94%,峰值时间比遗传算法低0.26 s,调节时间比遗传算法低1.4 s。这表明波浪补偿控制系统的控制效果与控制器的参数选择有很大关系。同时,粒子群优化算法的优化能够对波浪补偿PID控制系统进行整定,具有超调量小、响应时间快的优化效果。本文提出将粒子群算法应用于波浪补偿系统。验证了该方法应用后的优越性,为后续波浪补偿控制系统的研究提供了新的研究参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16cd/8319418/dd86f1cafd0c/41598_2021_93973_Fig1_HTML.jpg

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