2R1T并联机器人滑模自适应迭代学习控制  

Adaptive Sliding-mode Iterative Learning Control for 2R1T Parallel Robots

作　　者：周博文 张海峰[1] 李秦川[1] ZHOU Bowen;ZHANG Haifeng;LI Qinchuan(School of Mechanical Engineering,Zhejiang Sci-Tech University,Hangzhou 310018,China)

机构地区：[1]浙江理工大学机械工程学院,浙江杭州310018

出　　处：《机器人》2024年第3期317-329,共13页Robot

基　　金：国家自然科学基金(51935010)。

摘　　要：针对具有两转一移自由度的并联机器人的重复性加工轨迹跟踪精度问题,提出了一种滑模-自适应-迭代学习控制方案。具体而言,采用迭代学习控制来提高轨迹控制的精度,引入滑模控制来增强控制系统的鲁棒性,并采用自适应控制来处理系统内在的不确定性,降低了滑模控制中切换项引起的振荡问题。进一步,通过Bellman-Gronwall定理与λ范数证明了控制系统的收敛性。通过设计类李雅普诺夫函数证明了控制系统的稳定性。最后,进行控制方案的数值仿真以及真机实验,验证了所提出的控制方案能够有效提升两转一移类并联机构的控制精度以及系统鲁棒性。在该控制算法下,并联机器人关节空间的平均误差、最大误差及标准差相比于PD控制方法降低了59.5%、55.1%和60.1%。To solve the tracking accuracy problem in repetitive machining of parallel robots with 2 rotational and 1 translational(2R1T)degrees of freedom(DOFs),an adaptive sliding-mode iterative learning control scheme is proposed.Specifically,an iterative learning control method is adopted to improve the trajectory control accuracy,a sliding-mode control algorithm is introduced to enhance the robustness of the control system,and the adaptive control method is employed to deal with the intrinsic uncertainty of the control,therefore,overcoming the oscillation problem caused by switching terms in sliding-mode control.Furthermore,the convergence of the proposed control system is proved by Bellman-Gronwall theorem andλ-norm.The stability of the proposed control system is validated through designing a Lyapunov-like function.Finally,numerical simulations and prototype experiments are carried out to verify the proposed control scheme.The experimental results show that the proposed control scheme can significantly improve the control accuracy and the system robustness of the 2R1T parallel robots.Under this control algorithm,the mean error,maximum error and standard deviation of the joint space of the parallel robot are reduced by 59.5%,55.1%and 60.1%compared with the PD(proportional-derivative)control method.

关 键 词：迭代学习控制 2R1T并联机器人 滑模控制 自适应控制 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP273[自动化与计算机技术—控制科学与工程]