Neural network adaptive impedance control based on stiffness damping characteristics

doi:10.16731/j.cnki.1671-3133.2024.12.001

Abstract

Abstract: A neural network adaptive impedance control method based on stiffness damping characteristics was proposed to address the problem that the force tracking performance of impedance control for sanding robots was affected by the unknown environmental stiffness and the change of environmental position.Since the reference trajectory is not easy to be determined due to the unknown environmental parameters,an adaptive PI control law was constructed to compensate the reference trajectory and reduce the steady-state error of force tracking; in order to improve the dynamic performance of the force tracking control,according to the uniform regulation law of the force error on the stiffness coefficient and damping coefficient—stiffness damping characteristics,and combined with that the force error has the characteristics of time-varying and non-linear,an activation function describing the relationship between force error and stiffness damping characteristics was designed,and an adaptive impedance parameter neural network model was constructed,whose outputs were stiffness coefficient and damping coefficient,to ensure the suppleness of force tracking control through the impedance control based on the fusion of the reference trajectory compensation and an adaptive impedance parameter neural network model. The simulation results show that the proposed adaptive impedance control method has better force tracking effect than the traditional impedance control and the impedance control with reference trajectory PI compensation.

Key words: force-flexing control, unknown environment, reference trajectory compensation, variable impedance parameter model, neural network

CLC Number:

TH165
TP242

DANG Xuanju, HUANG Weijian. Neural network adaptive impedance control based on stiffness damping characteristics[J]. Modern Manufacturing Engineering, 2024, 531(12): 1-8.

References

[1] 李广,孔飞飞,王逍遥,等.基于多视觉传感器融合的装配过程质量移动智能检测技术研究[J].现代制造工程,2023(4):116-123.
[2] 徐天一,樊留群,王楠,等.基于激光追踪仪的机器人标定及误差分析研究[J].制造业自动化,2020,42(4):61-64.
[3] 李振,赵欢,王辉,等.机器人磨抛加工接触稳态自适应力跟踪研究[J].机械工程学报,2022,58(9):200-209.
[4] ROVEDA L,SHAHID A A,IANNACCI N,et al. Sensorless optimal interaction control exploiting environment stiffness estimation[J].IEEE Transactions on Control Systems Technology,2021,30(1):218-233.
[5] WU R,BILLARD A.Learning from demonstration and interactive control of variable-impedance to cut soft tissues[J].IEEE/ASME Transactions on Mechatronics,2021,27(5):2740-2751.
[6] LIU X,GE S S,ZHAO F,et al.Optimized interaction control for robot manipulator interacting with flexible environment[J].IEEE/ASME Transactions on Mechatronics,2020,26(6):2888-2898.
[7] 曾令城,李明富,杨真真,等.基于先验速度修正的工业机器人曲面跟踪柔顺控制[J].机械工程学报,2022,58(1):41-51.
[8] PAN Y,LI Z,SHI T,et al.Composite learning variable impedance robot control with stability and passivity guarantees[J].IEEE Robotics and Automation Letters,2023,9(1):119-126.
[9] ANAND A S,KAUSHIK R,GRAVDAHL J T,et al.Data-Efficient Reinforcement Learning for Variable Impedance Control[J].IEEE Access,2024,12(1):15631-15641.
[10] 郭万金,赵伍端,利乾辉,等.基于集成概率模型的变阻抗机器人打磨力控制[J].浙江大学学报(工学版),2023,57(12):2356-2366,2374.
[11] SUN T,PENG L,CHENG L,et al.Stability-guaranteed variable impedance control of robots based on approximate dynamic inversion[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2019,51(7):4193-4200.
[12] 梁旭,王卫群,苏婷婷,等.下肢康复机器人的主动柔顺自适应交互控制[J].机器人,2021,43(5):547-556.
[13] 李冬武,张洁,汪俊亮,等.细纱接头机器人神经网络自适应力跟踪导纳控制[J].机械工程学报,2023,59(11):221-231.
[14] HUANG H,YANG C,CHEN C L P.Optimal robot-environment interaction under broad fuzzy neural adaptive control[J].IEEE Transactions on Cybernetics,2020,51(7):3824-3835.
[15] 郭万金,于苏扬,田玉祥,等.机器人打磨自适应变阻抗主动柔顺恒力控制[J].哈尔滨工业大学学报,2023,55 (12):54-65.
[16] 王晓华,王育合,张蕾,等.缝纫机器人对织物张力与位置的模糊阻抗控制[J].纺织学报,2021,42(11):173-178.
[17] LACHNER J,ALLMENDINGER F,STRAMIGIOLI S,et al.Shaping impedances to comply with constrained task dynamics[J].IEEE Transactions on Robotics,2022,38(5):2750-2767.
[18] LI G,YU J,CHEN X.Adaptive fuzzy neural network command filtered impedance control of constrained robotic manipulators with disturbance observer[J].IEEE Transactions on Neural Networks and Learning Systems,2021,34(8):5171-5180.
[19] 杨国成.基于力反馈的打磨机器人去毛刺作业系统研究[D].广州:广东工业大学,2022.