Electric cylinder friction identification and compensation control based on improved gray wolf optimization algorithm

doi:10.16731/j.cnki.1671-3133.2024.08.006

Abstract

Abstract: Nonlinear friction for motorized cylinders is difficult to identify and adversely affects the tracking accuracy of the position of the six-degree-of-freedom Electrically Driven Stewart Platform (EDSP),an improved Gray Wolf Optimization algorithm introducing Levy flight and Cauchy variation (LCGWO) with Levy flight and Cauchy variation is proposed to identify the friction parameters of the electric cylinder,and a feed-forward compensation method based on the friction model is designed. Firstly,a mathematical model of EDSP considering the nonlinear friction of the electric cylinder is developed and the friction is described using the Lugre friction model. Then,considering that the nonlinear friction of the electric cylinder is difficult to identify,an LCGWO is proposed to identify the static and dynamic parameters in the friction model at the same time,and compared and analyzed with the general identification method. Finally,a feed forward compensation algorithm based on the Lugre friction model is designed and experimentally verified. The experimental results show that compared with other algorithms,LCGWO has better convergence speed and prediction accuracy;and the friction compensation of the electric cylinder by the friction feed-forward compensation method based on the friction model can effectively improve the platform′s position tracking accuracy.

Key words: Stewart platform, electric cylinder, parameter identification, GWO, friction compensation

CLC Number:

TP242.2

WANG Shuliang, WANG Yantao, ZHAO Mingwei, BIAN Jiazhi, LIU Lijun. Electric cylinder friction identification and compensation control based on improved gray wolf optimization algorithm[J]. Modern Manufacturing Engineering, 2024, 527(8): 42-50.

References

[1] HE Z,FENG X,ZHU Y,et al.Progress of stewart vibration platform in aerospace Micro-Vibration Control[J].Aero-space,2022,9(6):324.
[2] LIU J,CHEN X.Adaptive control based on neural network and beetle antennae search algorithm for an active heave compensation system[J].Int.J.Control Autom.Syst,2022,20(2):515-525.
[3] ARCONADA V S,GARCÍA-BARRUETABEÑA J,HAAS R.Validation of a ride comfort simulation strategy on an electric stewart platform for real road driving applications[J].Journal of Low Frequency Noise,Vibration and Active Control,2023,42(1):368-391.
[4] YANG S,MACLACHLAN R A,RIVIERE C N.Manipulator design and operation of a six-degree-of-freedom handheld tremor-canceling microsurgical instrument[J].IEEE/ASME Transactions on Mechatronics,2015,20(2):761-772.
[5] FENG H,YIN C,CAO D.Trajectory Tracking of an electro-hydraulic servo system with an new friction model-based compensation[J].IEEE/ASME Transactions on Mechatronics,2023,28(1):473-482.
[6] DAI K Y,ZHU Z C,SHEN G,et al.Adaptive force tracking control of electrohydraulic systems with low load using the modified LuGre friction model[J].Control Engineering Practice,2022,125:105213.
[7] 李明,封航,李莹月.基于改进遗传算法的LuGre摩擦模型参数辨识及补偿[J].组合机床与自动化加工技术,2018,537(11):38-42.
[8] 谭草,黎德祥,葛文庆,等.改进LuGre模型的电磁直线执行器自适应鲁棒控制[J].电机与控制学报,2022,26(10):130-138.
[9] DUMANLI A,SENCER B.Data-driven iterative trajectory shaping for precision control of flexible feed drives[J].IEEE/ASME Transactions on Mechatronics,2021,26(5):2735-2746.
[10] 李国丽,李浩霖,王群京,等.永磁球形电机Stribeck摩擦模型参数辨识[J].电机与控制学报,2022,26(4):121-130.
[11] 寻天柱.机器人关节摩擦的分数阶建模研究[D].武汉:华中科技大学,2022.
[12] 武诗睿,吴丹.基于摩擦力矩—速度曲线特定区域形状分析的LuGre摩擦参数辨识[J].清华大学学报(自然科学版),2022,62(9):1500-1507.
[13] ASADI F,SADATI S H.Full Dynamic Modeling of the general stewart platform manipulator via Kane’s method[J].Iran J Sci Technol Trans Mech Eng,2018,42(2):161-168.
[14] 高炳微,申伟,戴野,等.基于改进LuGre摩擦模型的阀控缸系统非线性控制策略研究[J].振动与冲击,2023,42(11):139-147,155.
[15] 李俊阳,赵琛,夏雨,等.基于改进LuGre摩擦模型的机器人关节模糊自适应反步控制[J].湖南大学学报(自然科学版),2022,49(10):147-156.
[16] SHARIFZADEH M,SENATORE A,FARNAM A,et al.A real-time approach to robust identification of tyre-road friction characteristics on mixed-mu roads[J].Vehicle system dynamics,2019,57(9):1338-1362.
[17] HU X,HAN S,LIU Y,et al.Two-Axis optoelectronic stabilized platform based on active disturbance Rejection controller with LuGre friction model[J].Electronics,2023,12(5):1261.
[18] 薛进学,郭清远,张丰收.基于LuGre摩擦模型前馈补偿的模糊PID控制系统设计[J].现代制造工程,2020(1):136-142.
[19] 崔靖凯,赛华阳.基于灰狼算法的模块化关节摩擦辨识和补偿[J].光学精密工程,2021,29(11):2683-2691.
[20] LIAO J,ZHOU F,ZHENG J.An improved parameter identification algorithm for the friction model of electro-hydraulic servo systems[J].Sensors,2023,23(4):2076.
[21] 杨贻俊,侯明.改进交叉算子遗传算法的摩擦模型辨识与补偿[J].微电机,2020,53(9):54-58.
[22] 王三秀,赵云波,陈光.基于神经网络的伺服机械手LuGre摩擦补偿控制[J].北京工业大学学报,2016,42(5):679-683.
[23] NADIMI-SHAHRAKI M H,TAGHIAN S,MIRJALILI S.An improved grey wolf optimizer for solving engineering problems[J].Expert Systems with Applications,2021,166:113917.
[24] 徐辰华,李成县,喻昕,等.基于Cat混沌与高斯变异的改进灰狼优化算法[J].计算机工程与应用,2017,53(4):1-9.
[25] 高炳微,申伟,戴野,等.基于改进萤火虫算法的摩擦模型参数辨识及补偿[J].振动与冲击,2023,42(6):69-78.