摘要
基于动力学模型的阻抗控制无须力传感器,可降低系统复杂性,实现机械臂的全臂柔顺,但现实系统的动力学模型往往难以精确确定。监督学习可以通过关节状态回归辨识动力学模型,但辨识精度取决于观测数据的数量和质量,且难以泛化到未观测空间。提出一种基于先验动力学知识的递归参数辨识方法,可提高数据效率及泛化能力。辨识过程结合递推牛顿欧拉动力学算法,逐一递归辨识关节参数,减少鞍点数量,克服辨识结果对初值的依赖性。在此基础上,设计了柔顺控制器;其外环为阻抗控制,通过辨识模型实现了无力传感器的全臂柔顺;内环采用滑模控制器,以辨识力矩作为动力学前馈,并通过径向基函数神经网络补偿系统的动态不确定性。实验结果表明,所提出的递归辨识算法可通过少量观测数据辨识完备的动力学模型,并实现全臂柔顺控制。
Although the impedance control based on the dynamic model is independent of force sensors,simplifying the robot’s complexity and realizing the whole-arm compliance,the real system’s accurate dynamic model is hard to formulate.Supervised learning can identify the model through joint state regression,but the accuracy depends on the observed data's quantity and quality,and the identification model was challenging generalization in unobserved space.A recursive parameter identification method with prior dynamics knowledge are proposed to improve data efficiency and model’s generalization.The joint’s parameters are identified recursively with the iterative Newton-Euler dynamics algorithm,which reduces the number of saddle points and overcomes the dependence on the initial values.On this foundation,the whole-arm compliance controller is designed.The outer loop is impedance control with the identified model to realize the whole arm compliance without force sensors.The inner loop is sliding mode control with the identified model as the dynamic feedforward,and the radial basis function neural network is used to compensate for the system's dynamic uncertainty.The results show that the recursive identification algorithm can identify the complete dynamic model with a few observation data and realize the robot’s whole-arm compliance.
作者
李洋
朱立爽
刘今越
郭士杰
LI Yang;ZHU Lishuang;LIU Jinyue;GUO Shijie(State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology,Tianjin 300130;Hebei Key Laboratory of Robot Sensing and Human-robot Interaction,Hebei University of Technology,Tianjin 300130;School of Mechanical Engineering,Hebei University of Technology,Tianjin 300130;China Automotive Technology&Research Center Co.Ltd.,Tianjin 300300)
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2022年第3期45-54,共10页
Journal of Mechanical Engineering
基金
国家重点研发计划(2016YFE0128700,2017YFB1301002)
河北省重点研发计划(18211816D)
河北省自然科学基金(E2017202270)资助项目。
关键词
全臂柔顺
先验动力学知识
递归参数辨识
辨识模型
whole-arm compliance
prior dynamics knowledge
recursive parameter identification
identification model
