Adaptive coordinated control strategy of multi manipulator system based on multi-agent

被引:0
|
作者
Zhu N. [1 ]
Han J. [2 ]
Xia L. [2 ]
Liu H. [1 ]
机构
[1] School of Mechatronics and Mould Engineering, Taizhou Vocational College of Science and Technology, Taizhou
[2] College of Mechanical Engineering, Hefei University of Technology, HeFei
关键词
Multi Manipulator System; Multi-Agent; PID control; Position Tracking; Variable Structure Control;
D O I
10.46300/9106.2021.15.126
中图分类号
学科分类号
摘要
With people's increasing awareness of life and the increasing complexity of exploration in unknown environment, a single robot can not meet the increasing demand, including the price, flexibility and efficiency of robots. As a common mechanical control system in industrial production instead of human production, multi manipulator system can be applied in complex environment, multi task and other conditions. In order to settle the coordinated control fault of multi manipulator system, we study adaptive coordinated control strategy with the help of multi-agent research method in this paper, which can simplify the complexity of the problem and design an efficient and feasible system control protocol. The complex items in the multi manipulator system are treated as non affine systems. Using the design idea of non affine algorithm, combined with implicit function theorem and median theorem, the non affine system is transformed into affine systems, the controller is separated, and a distributed adaptive control strategy is designed. The results indicate that manipulator systems can effectively track the active manipulator system in finite time and the significance of the algorithm is proven by MATLAB simulation analysis. © 2021, North Atlantic University Union NAUN. All rights reserved.
引用
收藏
页码:1159 / 1164
页数:5
相关论文
共 50 条
  • [21] An energy coordination control strategy for islanded microgrid based on a multi-agent system
    Luo, K. (luokui21@163.com), 1600, Automation of Electric Power Systems Press (37):
  • [22] Multi-agent manipulator control and moving obstacle avoidance
    Birbilis, GI
    Aspragathos, NA
    ON ADVANCES IN ROBOT KINEMATICS, 2004, : 441 - 448
  • [23] A Scripted-Control Integration Strategy in Multi-agent System
    Sun, Weihua
    Li, Qingshan
    Liu, He
    Wang, Lei
    Mao, Shaojie
    Li, Yuping
    2009 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, VOL I, PROCEEDINGS, 2009, : 263 - +
  • [24] Coordinated Control for Multi-Agent Systems Based on Networked Predictive Control Schemes
    Tan, Haoran
    Wu, Min
    Huang, Zhiwu
    PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC), 2016, : 2530 - 2535
  • [25] Dynamic Arterial Coordinated Control Based on Multi-agent Reinforcement Learning
    Fang, Liangliang
    Zhang, Weibin
    PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021), 2021, : 2716 - 2721
  • [26] A Coordinated Urban Traffic Signal Control Approach based on Multi-Agent
    Wei Wu
    Gong Shufeng
    Liu Hongxiu
    2009 INTERNATIONAL CONFERENCE ON INTELLIGENT ENGINEERING SYSTEMS, 2009, : 243 - 247
  • [27] Coordinated Ramp Metering Control Based on Multi-Agent Reinforcement Learning
    Tan, Jiyuan
    Qiu, Qianqian
    Guo, Weiwei
    2020 35TH YOUTH ACADEMIC ANNUAL CONFERENCE OF CHINESE ASSOCIATION OF AUTOMATION (YAC), 2020, : 492 - 498
  • [28] Decentralized Tribrid Adaptive Control Strategy for Simultaneous Formation and Flocking Configurations of Multi-agent System
    Prasad, B. K. Swathi
    Ramasangu, Hariharan
    Kadambi, Govind R.
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2022, 13 (06) : 274 - 282
  • [29] Distributed multi-agent based coordinated power management and control strategy for microgrids with distributed energy resources
    Rahman, M. S.
    Oo, A. M. T.
    ENERGY CONVERSION AND MANAGEMENT, 2017, 139 : 20 - 32
  • [30] Decentralized control for coordinated flow of multi-agent systems
    Crespi, V
    Cybenko, G
    Rus, D
    Santini, M
    PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3, 2002, : 2604 - 2609