Distributed synchronization for heterogeneous robots with uncertain kinematics and dynamics under switching topologies

被引:26
|
作者
Liu, Yen-Chen [1 ]
机构
[1] Natl Cheng Kung Univ, Dept Mech Engn, Tainan 70101, Taiwan
关键词
TASK-SPACE SYNCHRONIZATION; OUTPUT SYNCHRONIZATION; TIME-DELAY; SYSTEMS; STABILITY; CONSENSUS; NETWORKS; FEEDBACK; AGENTS;
D O I
10.1016/j.jfranklin.2014.11.018
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, the problems related to control for a network of heterogeneous robots, to achieve task-space synchronization in the presence of uncertainties in kinematic and dynamic models have been reported. Based on the proposed control algorithms and adaptive laws, networked robot systems can be ensured to synchronize with imprecise measurement of system parameters and communication delays. Three different connection scenarios, namely, strongly connected graphs, switching regular graphs, and jointly connected regular graphs have been considered in this paper. With the use of weighted storage function, attempts have been made in this paper to demonstrate that a multi-robot system, interconnected over a strongly connected graph with time delays, can be stabilized with guaranteed position and velocity synchronization. Since it is difficult to set up a fixed communication network between robots, an alternative synchronization controller was developed through switching topologies. Additionally, for interconnections between robots that are time-varying and agents that are disconnected at certain time intervals, the stability and synchronous behaviors of the networked system were studied using Lyapunov theory and switching control theory. Numerical examples were also provided to demonstrate the performance of the proposed multi-robot system. (C) 2014 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
引用
下载
收藏
页码:3808 / 3826
页数:19
相关论文
共 50 条
  • [1] Adaptive Synchronization Control of Cable-Driven Parallel Robots With Uncertain Kinematics and Dynamics
    Ji, Hao
    Shang, Weiwei
    Cong, Shuang
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2021, 68 (09) : 8444 - 8454
  • [2] Cascade Framework for Task-Space Synchronization of Networked Robots with Uncertain Kinematics and Dynamics
    Wang, Hanlei
    2012 12TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS & VISION (ICARCV), 2012, : 1489 - 1494
  • [3] An Emerging Dynamics Approach for Synchronization of Linear Heterogeneous Agents Interconnected Over Switching Topologies
    Adhikari, B.
    Morarescu, I. -C.
    Panteley, E.
    IEEE CONTROL SYSTEMS LETTERS, 2021, 5 (01): : 43 - 48
  • [4] Adaptive inverse dynamics control of robots with uncertain kinematics and dynamics
    Wang, Hanlei
    Xie, Yongchun
    AUTOMATICA, 2009, 45 (09) : 2114 - 2119
  • [5] Approximate Jacobian control for robots with uncertain kinematics and dynamics
    Cheah, CC
    Hirano, M
    Kawamura, S
    Arimoto, S
    IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, 2003, 19 (04): : 692 - 702
  • [6] Experiments on adaptive control of robots with uncertain kinematics and dynamics
    Cheah, CC
    Liu, C
    Slotine, JJE
    EXPERIMENTAL ROBOTICS IX, 2006, 21 : 57 - +
  • [7] Region reaching control for robots with uncertain kinematics and dynamics
    Cheah, C. C.
    Sun, Y. C.
    2006 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), VOLS 1-10, 2006, : 2577 - +
  • [8] Adaptive control of parallel robots with uncertain kinematics and dynamics
    Harandi, M. Reza J.
    Khalilpour, S. A.
    Taghirad, Hamid D.
    Romero, Jose Guadalupe
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2021, 157
  • [9] Optimal Model-Free Output Synchronization of Heterogeneous Multiagent Systems Under Switching Topologies
    Mu, Chaoxu
    Zhao, Qian
    Sun, Changyin
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2020, 67 (12) : 10951 - 10964
  • [10] Distributed Tracking Control of Uncertain Multiple Manipulators Under Switching Topologies Using Neural Networks
    Cheng, Long
    Cheng, Ming
    Yu, Hongnian
    Deng, Lu
    Hou, Zeng-Guang
    ADVANCES IN NEURAL NETWORKS - ISNN 2016, 2016, 9719 : 233 - 241