[an error occurred while processing this directive] [an error occurred while processing this directive] [an error occurred while processing this directive]
[an error occurred while processing this directive]

Journal of Projectiles, Rockets, Missiles and Guidance >

2019 , Vol. 39 >Issue 6: 75 - 82

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2019.06.017

Quantized Iterative Learning Control for Second-order Multi-agent Systems

  • DING Doujian 1 ,
  • ZHAO Xiaolin 2 ,
  • ZHAO Boxin 2 ,
  • GAO Guangen 3 ,
  • LIU Chang 1
Expand
  • 1 Graduate School, Air Force Engineering University, Xi’an 710051, China
  • 2 Equipment Management and UAV Engineering College, Air Force Engineering University, Xi’an 710051, China
  • 3 Aviation Key Laboratory of Science and Technology on Inertial Technology, AVIC, Xi’an 710065, China

Received date: 2018-11-12

  Online published: 2025-05-12

Fold

Abstract

This paper investigates the consensus tracking problem of second-order leader-following multi-agent systems.A logarithmic quantize is introduced to quantize the system state errors and the system states.And then two types of consensus errors based on quantized information are designed.Using the property of the logarithmic quantizes, the relation of the quantized density and consensus error can be obtained.Combining quantized information with iterative learning control approach, two iterative learning control protocols based on the consensus error are presented and the convergence condition for the consistency of a given system under the designed quantization protocol is derived.Finally, the presented control protocols are applied to the given system.By simulation and analysis, the results showed the effectiveness of the proposed method.

Key words： quantized error; control protocol; multi-agent systems; iterative learning control

Cite this article

DING Doujian , ZHAO Xiaolin , ZHAO Boxin , GAO Guangen , LIU Chang . Quantized Iterative Learning Control for Second-order Multi-agent Systems[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2019 , 39(6) : 75 -82 . DOI: 10.15892/j.cnki.djzdxb.2019.06.017

[an error occurred while processing this directive]

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
CHEN Hua. Robust stabilization for a class of dynamic feedback uncertain nonholonomic mobile robots with input saturation[J]. International Journal of Control Automation and Systems, 2014, 12(6):1216-1224.

[2]
XIAO Feng, WANG Long, CHEN Jie, et al. Finite-time formation control for multi-agent systems[J]. Automatica, 2009, 45(11):2605-2611.

[3]
REN Fenghui, ZHANG Minjie, SOETANTO D, et al. Conceptual design of a multi-agent system for interconnected power systems restoration[J]. IEEE Transactions on Power Systems, 2012, 27(2):732-740.

[4]
LI Hongjie, SU Hongye. Second-order consensus in multi-agent systems with directed topologies and communication constraints[J]. Neurocomputing, 2016, 173:942-952.

[5]
CHU Hongjun, CAI Yunze, ZHANG Weidong. Consensus tracking for multi-agent systems with directed graph via distributed adaptive protocol[J]. Neurocomputing, 2015, 166:8-13.

[6]
SUN Junyong, GENG Zhiyong. Adaptive consensus tracking for linear multi-agent systems with heterogeneous unknown nonlinear dynamics[J]. International Journal of Robust and Nonlinear Control, 2016, 26(1):154-173.

[7]
王玉振, 杜英雪, 王强. 多智能体时滞和无时滞网络的加权分组一致性分析[J]. 控制与决策, 2015, 30(11):1993-1998.

[8]
马忠军, 甘晓亮, 蒋贵荣. 具有时滞和时变系数的离散多智能体系统的一致性[J]. 控制与决策, 2016, 31(10):1785-1790.

[9]
YU Changbin, WANG Yinqiu, SHAO Jinliamg. Optimization of formation for multi-agent systems based on LQR[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(2):96-109.

[10]
吴忠强, 王洋. 分数阶不确定多智能体系统的鲁棒一致性控制[J]. 电机与控制学报, 2013, 17(4):97-103.

[11]
LIU Yang, JIA Yingmin. An iterative learning approach to formation control of multi-agent systems[J]. Systems & Control Letters, 2012, 61(1):148-154.

[12]
MENG Deyuan, JIA Yingmin. Formation control for multi-agent systems through an iterative learning design approach[J]. International Journal of Robust and Nonlinear Control, 2014, 24(2):340-361.

[13]
LIU Yang, JIA Yingmin. Formation control of discrete-ti me multi-agent systems by iterative learning approach[J]. International Journal of Control,Automation and Systems, 2012, 10(5):913-919.

[14]
MENG Deyuan, JIA Yingmin. Iterative learning approach es to design finite-time consensus protocols for multi-agentsystems[J]. Systems & Control Letters, 2012, 61(1):187-194.

[15]
LI Jinsha, LI Junmin. Iterative learning control approach for a kind of heterogeneous multi-agent systems with distributed initial state learning[J]. Applied Mathematics and Computation, 2015, 265:1044-1057.

[16]
LI Junmin, LI Jinsha. Adaptive fuzzy iterative learning co ntrol with initial-state learning for coordination control of leader-following multi-agent systems[J]. Fuzzy Sets and Systems, 2014, 248:122-137.

[17]
YANG Shiping, XU Jianxin, LI Xuefang. Iterative learning control with input sharing for multi-agent consensustracking[J]. Systems & Control Letters, 2016, 94:97-106.

[18]
XU Jin. Adaptive iterative learning control for high-order nonlinear multi-agent systems consensus tracking[J]. Systems & Control Letters, 2016, 89:16-23.

[19]
LI Jianzhen, YE Shuxia. Quantized consensus of first-order multi-agent systems under directed communication topologies[C]// IEEE.Proceedings of 2013 10th IEEE International Conference on Control and Automation (ICCA).[S.l.]:IEEE, 2013:1150-1153.]

[20]
WANG Yao, WU Qinghe, Wang Yinqiu, et al. Quantized consensus on first-order integrator networks[J]. Systems & Control Letters, 2012, 61(12):1145-1150.

[21]
REN Change, CHEN Long, CHEN C I Philip, et al. Qua ntized consensus control for second-order multi-agent systems with nonlinear dynamics[J]. Neurocomputing, 2016, 175:529-537.

[22]
CHEN Weisheng, LI Xiaobo, JIAO L C. Quantized consensus of second-order continuous-time multi-agent systems with a directed topology via sampled data[J]. Automatica, 2013, 49(7):2236-2242.

[23]
GUO Meng, DIMAROGONAS D V. Consensus with quantized relative statemeasurements[J]. Automatica, 2013, 49(8):2531-2537.

[24]
YIN Zhixiang, HUANG Yourui, GENG Xianya, et al. Consensus control for directed networks under quantized information exchange[J]. Asian Journal of Control, 2016, 18(2):763-770.

[25]
ZHANG Guoliang, SUN Yijie, ZENG Jing. Quantized consensus analysis for a class of heterogeneous multi-agent systems[C]// Control Theory Committee of China Automation Society.Proceedings of 2015 34th Chinese Control Conference (CCC).

[ S.l.:s.n.,2015:6703-6708.]

[26]
ZHANG Ting, LI Junmin. Robust iterative learning control of multi-agent systems with logarithmic quantizer[C]// Control Theory Committee of China Automation Society.Proceedings of 2015 34th Chinese Control Conference (CCC).

[ S.l.:s.n.,2015:7033-7038.]

[27]
ELIA N, MITTER S K. Stabilization of linear systems with limitedinformation[J]. IEEE Transactions on Automatic Control, 2001, 46(9):1384-1400.

[28]
FU M Y, XIE L H. The sector bound approach to quantized feedbackcontrol[J]. IEEE Transactions on Automatic Control, 2005, 50(11):1698-1711.

Options
Outlines

/

〈 〉

[an error occurred while processing this directive]