基于自适应粒子群遗传算法的柔性关节机器人动力学参数辨识

王跃灵; 旺玥; 王琪; 王洪斌

doi:10.3969/j.issn.1000-1158.2020.01.12

PDF(630 KB)

计量学报 ›› 2020, Vol. 41 ›› Issue (1) : 60-66. DOI: 10.3969/j.issn.1000-1158.2020.01.12

力学计量

基于自适应粒子群遗传算法的柔性关节机器人动力学参数辨识

王跃灵,旺玥,王琪,王洪斌

作者信息 +

Dynamic Parameter Identification of Flexible Joint Robot Based on Adaptive Particle Swarm Optimization-genetic Algorithm

WANG Yue-ling,WANG Yue,WANG Qi,WANG Hong-bin

Author information +

文章历史 +

摘要

提出一种基于自适应粒子群遗传算法的柔性关节机器人动力学参数辨识方法。该算法采用动态自适应调整策略,提高了粒子群算法收敛速度;同时引入新型遗传算法混合交叉变异机制,避免了粒子群陷入局部最优。将自适应粒子群遗传算法与标准粒子群算法、遗传算法、人工蜂群算法进行了比较,仿真实验结果表明该算法在迭代60次左右完成参数辨识,各参数的辨识相对误差均降低到了1%以内。最后利用旋转柔性关节实验平台进行了实验验证,实验结果证明了该算法具有更好的收敛速度和寻优精度。

Abstract

A method of dynamic parameter identification of flexible joint robot based on adaptive particle swarm genetic algorithm was proposed. The algorithm adopted dynamic adaptive adjustment strategy to improve the convergence speed of particle swarm algorithm. At the same time, a new hybrid genetic algorithm was introduced to avoid particle swarm optimization. Adaptive particle swarm genetic algorithm was compared with the standard particle swarm algorithm, genetic algorithm and artificial swarm algorithm, and the simulation results showed that the algorithm performs parameter identification after about 60 iterations, and the relative error of each parameter was reduced to less than 1%. Finally, the experimental verification was carried out by using the rotating flexible joint experimental platform, and the experimental results showed that the algorithm has better convergence speed and optimization precision.

导出引用

王跃灵,旺玥,王琪,王洪斌. 基于自适应粒子群遗传算法的柔性关节机器人动力学参数辨识[J]. 计量学报. 2020, 41(1): 60-66 https://doi.org/10.3969/j.issn.1000-1158.2020.01.12

WANG Yue-ling,WANG Yue,WANG Qi,WANG Hong-bin. Dynamic Parameter Identification of Flexible Joint Robot Based on Adaptive Particle Swarm Optimization-genetic Algorithm[J]. Acta Metrologica Sinica. 2020, 41(1): 60-66 https://doi.org/10.3969/j.issn.1000-1158.2020.01.12

中图分类号： TB93

参考文献

［1］张奇, 刘振, 谢宗武, 等. 具有谐波减速器的柔性关节参数辨识［J］. 机器人, 2014, 36(2): 164-170.
Zhang Q, Liu Z, Xie Z W, et al. Parameters Identification of Flexible Joints with Harmonic Driver ［J］. Robot, 2014, 36(2): 164-170.
［2］周军, 余跃庆. 考虑关节柔性的模块机器人动力学参数辨识［J］. 机器人, 2011, 33(4): 440-448.
Zhou J, Yu Y Q. Dynamic Parameter Identification of Modular Robot with Flexible Joints ［J］. Robot, 2011, 33(4):440-448.
［3］肖曦, 许青松, 王雅婷, 等. 基于遗传算法的内埋式永磁同步电机参数辨识方法［J］. 电工技术学报, 2014, 29(3): 21-26.
Xiao X, Xu Q S, Wang Y T, et al. Parameters Identification of Interior Permanent Magnet Synchronous Motors Based on Genetic Algorithm ［J］. Transactions of China Electrotechnical Society, 2014, 29(3): 21-26.
［4］赵洋, 韦莉, 张逸成, 等. 基于粒子群优化的超级电容器模型结构与参数辨识［J］. 中国电机工程学报, 2012, 32(15):155-161.
Zhao Y, Wei L, Zhang Y C, et al. Structure and Parameter Identification of Supercapacitors Based on Particle Swarm Optimization ［J］. Proceedings of CSEE, 2012, 32(15): 155-161.
［5］马欢, 李文皓, 肖歆昕, 等. 空间机器人惯性参数辨识的粒子群优化新算法［J］. 宇航学报, 2015, 36(3): 278-283.
Ma H, Li W H, Xiao X X, et al. A New Particle Swarm Optimization Approach to the Inertia Parameters Identification of Onorbit Space Robot ［J］. Journal of Astronauties, 2015, 36(3): 278-283.
［6］冯楠, 王振臣, 胖莹. 基于自适应遗传算法和 BP 神经网络的电池容量预测［J］. 计量学报, 2012, 33(6): 546-549.
Feng N, Wang Z C, Pang Y. BP Neural Networks Based on Adaptive Genetic Algorithms and Its Application to Prediction of Battery Capacity ［J］. Acta Metrologica Sinica, 2012, 33(6): 546-549.
［7］滕峰成, 郝宇, 林晓乐. 基于PSO算法的MFF模型的参数辨识与优化［J］. 计量学报, 2017, 38(2): 209-214.
Teng F C, Hao Y, Lin X L. Parameter Identification and Optimization of the MFF Model Based on the Particle Swarm Optimization Algorithm ［J］. Acta Metrologica Sinica, 2017, 38(2): 209-214.
［8］Spong M W. Modeling and control of elastic joint robots ［J］. Journal of dynamic systems, measurement, and control, 1987, 109(4): 310-319.
［9］Flacco F,De Luca A.A pure signal-based stiffness estimation for VSA devices ［C］//Robotics and Automation (ICRA), 2014 IEEE International Conference on. IEEE, 2014: 2418-2423.
［10］郭瑞, 王璇, 林思建, 等. 一种改进混沌粒子群优化的节点定位算法［J］. 计量学报, 2015, 36(2): 202-206.
Guo R, Wang X, Lin S J, et al. An Improved Chaotic Particle Swarm Optimization Node Localization Algrithm ［J］. Acta Metrologica Sinica, 2015, 36(2): 202-206.
［11］朱大奇, 袁义丽, 邓志刚. 水下机器人参数辨识的量子粒子群算法［J］. 控制工程, 2015, 22(3): 531-537.

Zhu D Q, Yuan Y L, Deng Z G. Parameter Identification of Underwater Vehicles Based on the Quantum-behaved Particle Swarm Optimization Algorithm ［J］. Control Engineering of China, 2015, 22(3): 531-537.
［12］傅莹, 刘舒然. 机器人转动关节的LuGre摩擦模型参数辨识［J］. 微型机与应用, 2014, 33(19): 76-78.
Fu Y, Y Liu S R. Identification for parameters of the LuGre friction model in the robot rotating joints ［J］. Technique and Method, 2014, 33(19): 76-78.
［13］张强,宋爱国,刘玉庆, 等. 一种指尖三维力传感器设计［J］. 计量学报, 2018, 39(1): 52-55.
Zhang Q, Song A G, Liu Y Q, et al. Design of a Three Dimensional Force Sensor［J］. Acta Metrologica Sinica, 2018, 39(1): 52-55.
［14］张旭辉, 林海军, 刘明珠, 等. 基于蚁群粒子群优化的卡尔曼滤波算法模型参数辨识［J］. 电力系统自动化, 2014, 38(4): 44-50.
Zhang X H, Lin H J, Liu M Z, et al. Model Parameters Identification of UKF Algorithm Based on ACO-PSO ［J］. Automation of Electric Power Systems, 2014, 38(4): 44-50.
［15］吴忠强, 杜春奇, 张伟, 等. 基于改进布谷鸟搜索算法的永磁同步电机参数辨识［J］. 计量学报, 2017, 38(5): 631-636.
Wu Z Q, Du C Q, Zhang W, et al. The Parameter Identification of Permanent Magnet Synchronous Motor Based on Improved Cuckoo Search Algorithm ［J］. Acta Metrologica Sinica, 2017, 38(5): 631-636.
［16］Ménard T, Grioli G, Bicchi A. A Stiffness Estimator for Agonistic-Antagonistic Variable-Stiffness-Actuator Devices ［J］. IEEE Transactions on Robotics, 2014, 30(5): 1269-1278.
［17］Grioli G, Bicchi A. A real-time parametric stiffness observer for VSA devices ［C］//Robotics and Automation (ICRA), 2011 IEEE International Conference on. IEEE, 2011: 5535-5540.
［18］Kircanski N M, Goldenberg A A. An experimental study of nonlinear stiffness, hysteresis, and friction effects in robot joints with harmonic drives and torque sensors ［J］. The International Journal of Robotics Research, 1997, 16(2): 214-239.