Disturbance Observer-Based Neural Network Control of Cooperative Multiple Manipulators With Input Saturation

被引:104
作者
He, Wei [1 ]
Sun, Yongkun [1 ]
Yan, Zichen [1 ]
Yang, Chenguang [2 ]
Li, Zhijun [3 ]
Kaynak, Okyay [4 ,5 ]
机构
[1] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Inst Artificial Intelligence, Beijing 100083, Peoples R China
[2] Univ West England, Bristol Robot Lab, Bristol BS16 1QY, Avon, England
[3] Univ Sci & Technol China, Dept Automat, Hefei 230026, Peoples R China
[4] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
[5] Bogazici Univ, TR-34342 Istanbul, Turkey
基金
英国工程与自然科学研究理事会; 北京市自然科学基金; 中国国家自然科学基金;
关键词
Manipulator dynamics; Robot kinematics; Neural networks; Kinematics; Force; Adaptive neural network control; distubance observer; input saturation; multi-manipulator collaborative control; robot; HUMAN-ROBOT INTERACTION; NONLINEAR-SYSTEMS; IMPEDANCE CONTROL; VIBRATION CONTROL; ADAPTIVE-CONTROL; FEEDBACK-CONTROL; CONTROL DESIGN; ROBUST-CONTROL; CONTROL SCHEME; EXOSKELETON;
D O I
10.1109/TNNLS.2019.2923241
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, the complex problems of internal forces and position control are studied simultaneously and a disturbance observer-based radial basis function neural network (RBFNN) control scheme is proposed to: 1) estimate the unknown parameters accurately; 2) approximate the disturbance experienced by the system due to input saturation; and 3) simultaneously improve the robustness of the system. More specifically, the proposed scheme utilizes disturbance observers, neural network (NN) collaborative control with an adaptive law, and full state feedback. Utilizing Lyapunov stability principles, it is shown that semiglobally uniformly bounded stability is guaranteed for all controlled signals of the closed-loop system. The effectiveness of the proposed controller as predicted by the theoretical analysis is verified by comparative experimental studies.
引用
收藏
页码:1735 / 1746
页数:12
相关论文
共 77 条
  • [1] Introduction
    不详
    [J]. DIABETES CARE, 2015, 38 : S1 - S2
  • [2] [Anonymous], [No title captured]
  • [3] [Anonymous], 1995, PID CONTROLLERS THEO
  • [4] LEARNING WITHOUT LOCAL MINIMA IN RADIAL BASIS FUNCTION NETWORKS
    BIANCHINI, M
    FRASCONI, P
    GORI, M
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS, 1995, 6 (03): : 749 - 756
  • [5] Internal force-based impedance control for cooperating manipulators
    Bonitz, RG
    Hsia, TC
    [J]. IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, 1996, 12 (01): : 78 - 89
  • [6] Six-DOF Impedance Control of Dual-Arm Cooperative Manipulators
    Caccavale, Fabrizio
    Chiacchio, Pasquale
    Marino, Alessandro
    Villani, Luigi
    [J]. IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2008, 13 (05) : 576 - 586
  • [7] Cai YF, 2016, IEEE-CAA J AUTOMATIC, V3, P15
  • [8] Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks
    Chandwani, Vinay
    Agrawal, Vinay
    Nagar, Ravindra
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (02) : 885 - 893
  • [9] Composite nonlinear feedback control for linear systems with input saturation: Theory and an application
    Chen, BM
    Lee, TH
    Peng, KM
    Venkataramanan, V
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2003, 48 (03) : 427 - 439
  • [10] Visual servoing of dynamic wheeled mobile robots with anti-interference finite-time controllers
    Chen, Hua
    Chen, Lei
    Zhang, Qian
    Tong, Fei
    [J]. ASSEMBLY AUTOMATION, 2018, 38 (05) : 558 - 567