基于LQR和UKF的软体机器人无模型轨迹跟踪控制

doi:10.3969/j.issn.1004-132X.2025.03.020

中国机械工程 ›› 2025, Vol. 36 ›› Issue (03): 570-575,583.DOI: 10.3969/j.issn.1004-132X.2025.03.020

基于LQR和UKF的软体机器人无模型轨迹跟踪控制

关胜闯¹;柳宇钧²;杨清昊¹;刘兆冰¹*

1.武汉理工大学机电工程学院，武汉，430000
2.宁波诺丁汉大学电气与电子工程系，宁波，315000

出版日期:2025-03-25 发布日期:2025-04-23
作者简介:关胜闯，男，1998年生，硕士研究生。研究方向为软体机器人建模与控制。E-mail:giky0915@163.com。
基金资助:
国家大学生创新创业训练计划（024104970）

Model-free Trajectory Tracking Control of Soft Robots Based on LQR and UKF

GUAN Shengchuang¹;LIU Yujun²;YANG Qinghao¹;LIU Zhaobing¹*

1.School of Mechanical and Electrical Engineering,Wuhan University of Technology,Wuhan,430000
2.Department of Electrical and Electronic Engineering,University of Nottingham Ningbo China,
Ningbo,Zhejiang,315000

Online:2025-03-25 Published:2025-04-23

摘要/Abstract

摘要： 针对软体机器人精确建模和控制问题提出一种新颖的非线性估计和控制策略，用于控制二维气动软体机器人的动态性能。采用基于Koopman算子的数据驱动方法建立二维气动软体机器人的线性模型。利用无迹卡尔曼滤波器(UKF)进行传感器数据滤波和系统状态估计，同时利用线性二次型调节器(LQR)来实现轨迹跟踪的最优控制。仿真和实验比较结果一致表明，所提方法在轨迹跟踪性能方面优于另两种方法。

关键词: 软体机器人, Koopman算子, LQR控制, 无迹卡尔曼滤波器

Abstract: A novel nonlinear estimation and control strategy for controlling the dynamic performance of a 2D pneumatic soft robot was proposed to address the problems of accurate modelling and control of soft robots. Firstly, a linear model of the 2D pneumatic soft robot was established using a Koopman operator-based approach. Then, the UKF was proposed for sensor data filtering and system state estimation, while the LQR was used for optimal control of trajectory tracking. Simulation and experimental results consistently show that the strategy herein performs better than other two strategies in terms of trajectory tracking.

Key words: soft robot, Koopman operator, linear quadratic regulator(LQR) control, unscented Kalman filter(UKF)

中图分类号:

TP242

关胜闯1, 柳宇钧2, 杨清昊1, 刘兆冰1. 基于LQR和UKF的软体机器人无模型轨迹跟踪控制[J]. 中国机械工程, 2025, 36(03): 570-575,583.

GUAN Shengchuang1, LIU Yujun2, YANG Qinghao1, LIU Zhaobing1. Model-free Trajectory Tracking Control of Soft Robots Based on LQR and UKF[J]. China Mechanical Engineering, 2025, 36(03): 570-575,583.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.cmemo.org.cn/CN/10.3969/j.issn.1004-132X.2025.03.020

https://www.cmemo.org.cn/CN/Y2025/V36/I03/570

参考文献

［1］LIU Zhaobing, PENG Kerui, HAN Lvpeng, et al. Modeling and Control of Robotic Manipulators Based on Artificial Neural Networks：a Review［J］. Iranian Journal of Science and Technology, Transactions of Mechanical Engineering, 2023, 47(4)：1307-1347.
［2］徐丰羽, 孟凡昌, 范保杰, 等. 软体机器人驱动、建模与应用研究综述［J］. 南京邮电大学学报(自然科学版), 2019, 39(3)：64-75.
XU Fengyu, MENG Fanchang, FAN Baojie, et al. Review of Driving Methods, Modeling and Application in Soft Robots［J］. Journal of Nanjing University of Posts and Telecommunications (Natural Science Edition), 2019, 39(3)：64-75.
［3］SCHEGG P, DURIEZ C. Review on Generic Methods for Mechanical Modeling, Simulation and Control of Soft Robots［J］. PLoS One, 2022, 17(1)：e0251059.
［4］BRUDER D, FU Xun, GILLESPIE R B, et al. Data-driven Control of Soft Robots Using Koopman Operator Theory［J］. IEEE Transactions on Robotics, 2021, 37(3)：948-961.
［5］NARASINGAM A, KWON J S I. Application of Koopman Operator for Model-based Control of Fracture Propagation and Proppant Transport in Hydraulic Fracturing Operation［J］. Journal of Process Control, 2020, 91：25-36.
［6］CAO Guizhou, HUO Benyan, YANG Lei, et al. Model-based Robust Tracking Control without Observers for Soft Bending Actuators［J］. IEEE Robotics and Automation Letters, 2021, 6(3)：5175-5182.
［7］XAVIER M S, FLEMING A J, YONG Y K. Nonlinear Estimation and Control of Bending Soft Pneumatic Actuators Using Feedback Linearization and UKF［J］. IEEE/ASME Transactions on Mechatronics, 2022, 27(4)：1919-1927.
［8］WILLIAMS M O, KEVREKIDIS I G, ROWLEY C W. A Data-driven Approximation of the Koopman Operator：Extending Dynamic Mode Decomposition［J］. Journal of Nonlinear Science, 2015, 25(6)：1307-1346.
［9］NAIDU D S. Optimal Control Systems［M］. Boca Raton：CRC Press, 2018.

[1]	许万, 程兆, 夏瑞东, 陈汉成. 一种基于动态残差的自适应鲁棒无迹卡尔曼滤波器定位算法[J]. 中国机械工程, 2023, 34(21): 2607-2614.
[2]	丛明, 毕聪, 王明昊, 刘冬, 杜宇. 面向手功能康复训练的软体机器人设计[J]. 中国机械工程, 2022, 33(08): 883-889.
[3]	闫丽萍, 张华良, 董学智, 陈海生, 谭春青, . 燃气轮机气路与传感器耦合故障诊断模型研究[J]. 中国机械工程, 2021, 32(24): 2960-2966，2974.
[4]	张伟, 马恺骐, 金国庆, . 面向软体机器人的嵌入式3D打印聚二甲基硅氧烷材料实验研究[J]. 中国机械工程, 2021, 32(19): 2357-2366.
[5]	范需;戴宁;王宏涛;丁龙伟;谢绍辉. 气动网格软体驱动器弯曲变形预测方法[J]. 中国机械工程, 2020, 31(09): 1108-1114.
[6]	陈尤旭1,2;王德山1;张伟1,2;金国庆1,2. 面向软体机器人的3D打印硅胶软材料实验研究[J]. 中国机械工程, 2020, 31(05): 603-609,629.

基于LQR和UKF的软体机器人无模型轨迹跟踪控制

Model-free Trajectory Tracking Control of Soft Robots Based on LQR and UKF

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

编辑推荐

Metrics