Recursive terminal sliding mode based control of robot manipulators with a novel sliding mode disturbance observer

被引:7
|
作者
Song, Tangzhong [1 ]
Fang, Lijin [1 ]
Zhang, Yue [2 ]
Shen, Hesong [1 ]
机构
[1] Northeastern Univ, Fac Robot Sci & Engn, Shenyang, Peoples R China
[2] Northeastern Univ, Sch Mech Engn & Automat, Shenyang, Peoples R China
基金
中国国家自然科学基金;
关键词
Recursive terminal sliding mode; Model-based control; Sliding mode disturbance observer; Trajectory tracking; Robot manipulator; TRACKING CONTROL; TRAJECTORY TRACKING; MOTOR;
D O I
10.1007/s11071-023-09136-9
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
This paper investigates the high-precision sliding mode based tracking control of robot manipulators with uncertain dynamics and external disturbances. Different from most currently used fast nonsingular terminal sliding mode surfaces (FNTSMs) which use linear sliding mode (LSM) to avoid singularity, a new recursive terminal sliding mode surface (RTSM) is firstly constructed with a recursive structure to avoid the singularity problem in this paper. The RTSM can improve convergence precision and speed near the equilibrium point compared with FNTSMs. Then a sliding mode based controller has been designed to stabilize the closed-loop system. To cope with model uncertainties, frictions and external disturbances, a novel adaptive sliding mode disturbance observer (ASMDO) has been constructed, which can estimate lumped uncertainties and feed them to the controller to achieve disturbance-rejection control. Compared with traditional disturbance observer with asymptotic stability, the observation error of ASMDO can be driven into a sufficiently small set centered on zero within a predefined fixed time, which means fast observation speed and high observation accuracy. The upper bounds of uncertainties and their derivatives are not needed in observer design. Abundant simulations and experiments also verified the effectiveness and superior properties of the proposed scheme.
引用
收藏
页码:1105 / 1121
页数:17
相关论文
共 50 条
  • [41] Adaptive Neural Network Observer Based PID-Backstepping Terminal Sliding Mode Control for Robot Manipulators
    Xi, Ruidong
    Yang, Zhixin
    Xiao, Xiao
    2020 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM), 2020, : 209 - 214
  • [42] Non-Singular Fast Terminal Sliding Mode Control With Disturbance Observer for Underactuated Robotic Manipulators
    Mobayen, Saleh
    Mostafavi, Soheila
    Fekih, Afef
    IEEE ACCESS, 2020, 8 (08): : 198067 - 198077
  • [43] Terminal and Backstepping Sliding Mode Control with Genetic Algorithms for Robot Manipulators
    Tilki, Umut
    Olgun, Melikcan
    STUDIES IN INFORMATICS AND CONTROL, 2023, 32 (02): : 117 - 126
  • [44] A new nonsingular integral terminal sliding mode control for robot manipulators
    Su, Yuxin
    Zheng, Chunhong
    INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2020, 51 (08) : 1418 - 1428
  • [45] Cascade Terminal Sliding Mode Control for PMSM with Nonlinear Disturbance Observer
    Che, Xin
    Tian, Dapeng
    Jia, Ping
    2021 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS (ICM), 2021,
  • [46] Terminal Sliding Mode Control with Disturbance Observer for Autonomous Mobile Robots
    Xu Kun
    Chen Mou
    2015 34TH CHINESE CONTROL CONFERENCE (CCC), 2015, : 765 - 770
  • [47] Sliding Mode Control Using Disturbance Observer for a Flexible Link Robot
    Han, Linyan
    Wu, Qingxian
    Chen, Mou
    Li, Xiaoran
    2016 14TH INTERNATIONAL WORKSHOP ON VARIABLE STRUCTURE SYSTEMS (VSS), 2016, : 448 - 453
  • [48] Robust Sliding Mode Control for Robot Manipulators
    Islam, Shafiqul
    Liu, Xiaoping P.
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2011, 58 (06) : 2444 - 2453
  • [49] Sliding mode control for quadrotor with disturbance observer
    Ahmed, Nigar
    Chen, Mou
    ADVANCES IN MECHANICAL ENGINEERING, 2018, 10 (07):
  • [50] Recursive sliding mode control with adaptive disturbance observer for a linear motor positioner
    Shao, Ke
    Zheng, Jinchuan
    Wang, Hai
    Xu, Feng
    Wang, Xueqian
    Liang, Bin
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2021, 146