Journal of Guangxi Teachers Education University (Philosophy and Social Sciences Edition) ›› 2019, Vol. 37 ›› Issue (3): 42-49.doi: 10.16088/j.issn.1001-6600.2019.03.005

Previous Articles    

ADRC Controller Optimization Design Based on Two-space PSO Algorithm for Quad-rotor UAV

CHEN Linqi, LI Tinghui*   

  1. College of Electronic Engineering,Guangxi Normal University,Guilin Guangxi 541004,China
  • Published:2019-07-12

Abstract: The traditional quad-rotor UAV active disturbance rejection controller(ADRC)requires many parameters, and the traditional PSO algorithm can effectively set the parameters of the controller, but it requires a large number of particle groups and slows down the optimization speed. In this paper, based on the traditional PSO algorithm, according to the characteristics of the ADRC, the ADRC parameters are divided into two groups, and initialized the particle groups in two spaces, and cross-optimization is carried out to form a two-space PSO algorithm. Simulation results show that, compared with the traditional PSO algorithm, the two-space PSO algorithm requires fewer particle groups and has a faster optimization speed, so the ADRC optimized by the two-space PSO algorithm has a smaller error and a faster convergence speed.

Key words: PSO algorithm, two-space, ADRC, parameter tuning, control optimization

CLC Number: 

  • TP242
[1] 王丽君,李擎,童朝南,等.时滞系统的自抗扰控制综述[J].控制理论与应用,2013,30(12):1521-1533.DOI: 10.7641/CTA.2013.31058.
[2] GAO Zhiqiang.Scaling and bandwidth-parameterization based controller tuning[C]//Proceedings of the 2003 American Control Conference.Piscataway NJ:IEEE Press,2003:4989-4996.DOI:10.1109/ACC.2003.1242516.
[3] FU Caifen,TAN Wen.A new method to tune linear active disturbance rejection[C]//Proceedings of the 2016 American Control Conference.Piscataway NJ:IEEE Press,2016:1560-1565.DOI:10.1109/ACC.2016.7525138.
[4] 朱启轩,张红刚,高军科.光电稳定平台神经网络自抗扰控制方法[J].电光与控制,2018,25(3):10-14.DOI: 10.3969/j.issn.1671-637X.2018.03.003.
[5] 齐晓慧,李杰,韩帅涛.基于BP神经网络的自适应自抗扰控制及仿真[J].兵工学报,2013,34(6):776-782.DOI: 10.3969/j.issn.1000-1093.2013.06.019.
[6] KENNEDY J,EBERHART R.Particle swarm optimization[C]//Proceedings of 1995 IEEE International Conference on Neural Networks.Piscataway NJ:IEEE Press,1995:1942-1948.DOI: 10.1109/ICNN.1995.488968.
[7] 武俊峰,姜欣辰.基于粒子群算法的直线二级倒立摆LQR控制器优化控制方法[J].黑龙江科技大学学报,2018,28(5):570-576.DOI:10.3969/j.issn.2095-7262.2018.05.017.
[8] 胡丹丹,张宇辰.基于改进粒子群算法的四旋翼自抗扰控制器优化设计[J/OL].计算机应用研究,2019,36(7) (2018-4-12)[2018-10-24].http://www.arocmag.com/article/02-2019-07-022.html.DOI:10.3969/j.issn.1001-3695.2018.01.0026.
[9] 楚玉华,黄巧亮.基于双粒子群算法的船舶电力系统网络重构[J].电子设计工程,2017,25(5):37-41.DOI: 10.14022/j.cnki.dzsjgc.2017.05.010.
[10]黄文俊.基于优化ADRC的伺服控制技术的研究与开发[D].无锡:江南大学,2017.
[11]王建民,邓展,杨刚.SOA寻优算法在磨机给料控制系统的研究与仿真[J].华北理工大学学报(自然科学版),2017, 39(4):94-100.DOI:10.3969/j.issn.2095-2716.2017.04.016.
[12]韩京清.自抗扰控制技术:估计补偿不确定因素的控制技术[M].北京:国防工业出版社,2008.
[13]XU Rong,ZGNER .Sliding mode control of a class of underactuated systems[J].Automatica,2008, 44(1):233-241.DOI:10.1016/j.automatica.2007.05.014.
No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!