Full Text:   <584>

Summary:  <145>

CLC number: TP273

On-line Access: 2018-11-11

Received: 2016-12-11

Revision Accepted: 2017-03-23

Crosschecked: 2018-09-09

Cited: 0

Clicked: 1722

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jing-lin Hu

https://orcid.org/0000-0003-1566-0848

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.9 P.1086-1097

10.1631/FITEE.1601801


Adaptive output feedback formation tracking for a class of multiagent systems with quantized input signals


Author(s):  Jing-lin Hu, Xiu-xia Sun, Lei He, Ri Liu, Xiong-feng Deng

Affiliation(s):  Equipment Management and UAV Engineering College, Air Force Engineering University, Xi’an 710038, China

Corresponding email(s):   hujinglineee@163.com

Key Words:  Multiagent system, Adaptive output feedback, Formation tracking, Hysteretic quantizer


Jing-lin Hu, Xiu-xia Sun, Lei He, Ri Liu, Xiong-feng Deng. Adaptive output feedback formation tracking for a class of multiagent systems with quantized input signals[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(9): 1086-1097.

@article{title="Adaptive output feedback formation tracking for a class of multiagent systems with quantized input signals",
author="Jing-lin Hu, Xiu-xia Sun, Lei He, Ri Liu, Xiong-feng Deng",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="19",
number="9",
pages="1086-1097",
year="2018",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601801"
}

%0 Journal Article
%T Adaptive output feedback formation tracking for a class of multiagent systems with quantized input signals
%A Jing-lin Hu
%A Xiu-xia Sun
%A Lei He
%A Ri Liu
%A Xiong-feng Deng
%J Frontiers of Information Technology & Electronic Engineering
%V 19
%N 9
%P 1086-1097
%@ 2095-9184
%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601801

TY - JOUR
T1 - Adaptive output feedback formation tracking for a class of multiagent systems with quantized input signals
A1 - Jing-lin Hu
A1 - Xiu-xia Sun
A1 - Lei He
A1 - Ri Liu
A1 - Xiong-feng Deng
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
IS - 9
SP - 1086
EP - 1097
%@ 2095-9184
Y1 - 2018
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601801


Abstract: 
A novel adaptive output feedback control approach is presented for formation tracking of a multiagent system with uncertainties and quantized input signals. The agents are described by nonlinear dynamics models with unknown parameters and immeasurable states. A high-gain dynamic state observer is established to estimate the immeasurable states. With a proper design parameter choice, an adaptive output feedback control method is developed employing a hysteretic quantizer and the designed dynamic state observer. Stability analysis shows that the control strategy can guarantee that the agents can maintain the formation shape while tracking the reference trajectory. In addition, all the signals in the closed-loop system are bounded. The effectiveness of the control strategy is validated by simulation.

考虑量化输入信号的多智能体系统自适应输出反馈编队跟踪控制

摘要:针对带有不确定性和量化输入信号的多智能体系统的编队跟踪控制问题,提出一种自适应输出反馈控制方法。利用非线性模型描述智能体系统,其中包含未知参数和不可测状态。建立一个高增益状态观测器以估测系统不可测状态。在高增益观测器和迟滞量化器基础上,设计自适应输出反馈控制方法,并确定控制系统的参数设置方法。稳定性分析证明该控制方法能使多智能体系统跟踪参考航迹并保持编队队形。同时,闭环系统中的所有信号均能保持有界。最后,仿真实验表明该控制方法的有效性。

关键词:多智能体系统;自适应输出反馈;编队跟踪;迟滞量化

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Bae J, Kim Y, 2012. Adaptive controller design for spacecraft formation flying using sliding mode controller and neural networks. J Franklin Inst, 349(2):578-603.

[2]Briñón-Arranz L, Seuret A, Canudas-de-Wit C, 2014. Cooperative control design for time-varying formations of multi-agent systems. IEEE Trans Autom Contr, 59(8): 2283-2288.

[3]Fu JJ, Wang JZ, 2014. Adaptive coordinated tracking of multi-agent systems with quantized information. Syst Contr Lett, 74:115-125.

[4]Hayakawa T, Ishii H, Tsumura K, 2009. Adaptive quantized control for nonlinear uncertain systems. Syst Contr Lett, 58(9):625-632.

[5]He L, Sun XX, Lin Y, 2017. Distributed adaptive control for time-varying formation tracking of a class of networked nonlinear systems. Int J Contr, 90(7):1319-1326.

[6]He WL, Zhang B, Han QL, et al., 2017. Leader-following consensus of nonlinear multiagent systems with stochastic sampling. IEEE Trans Cybern, 47(2):327-338.

[7]Jiang ZP, Liu TF, 2013. Quantized nonlinear control—a survey. Acta Autom Sin, 39(11):1820-1830.

[8]Li GQ, Lin Y, Zhang X, 2017. Adaptive output feedback control for a class of nonlinear uncertain systems with quantized input signal. Int J Rob Nonl Contr, 27(1):169-184.

[9]Liu JL, Elia N, 2004. Quantized feedback stabilization of non-linear affine systems. Int J Contr, 77(3):239-249.

[10]Liu TF, Jiang ZP, Hill DJ, 2012. A sector bound approach to feedback control of nonlinear systems with state quantization. Automatica, 48(1):145-152.

[11]Liu Y, Jia Y, 2012. Adaptive leader-following consensus control of multi-agent systems using model reference adaptive control approach. IET Contr Theory Appl, 6(13): 2002-2008.

[12]Liu YF, Zhao Y, Chen GR, 2016. Finite-time formation tracking control for multiple vehicles: a motion planning approach. Int J Rob Nonl Contr, 26(14):3130-3149.

[13]Lu XQ, Wang YN, Mao JX, 2014. Nonlinear control for multi-agent formations with delays in noisy environments. Acta Autom Sin, 40(12):2959-2967.

[14]Mahmood A, Kim Y, 2015. Leader-following formation control of quadcopters with heading synchronization. Aerosp Sci Technol, 47:68-74.

[15]Praly L, Jiang ZP, 2004. Linear output feedback with dynamic high gain for nonlinear systems. Syst Contr Lett, 53(2): 107-116.

[16]Rezaee H, Abdollahi F, 2014. A decentralized cooperative control scheme with obstacle avoidance for a team of mobile robots. IEEE Trans Ind Electron, 61(1):347-354.

[17]Wan Y, Wen GH, Cao JD, et al., 2016. Distributed node-to-node consensus of multi-agent systems with stochastic sampling. Int J Rob Nonl Contr, 26(1):110-124.

[18]Wan Y, Cao JD, Wen GH, 2017. Quantized synchronization of chaotic neural networks with scheduled output feedback control. IEEE Trans Neur Netw Learn Syst, 28(11):2638-2647.

[19]Wen GH, Yu WW, Hu GQ, et al., 2015. Pinning synchronization of directed networks with switching topologies: a multiple Lyapunov functions approach. IEEE Trans Neur Netw Learn Syst, 26(12):3239-3250.

[20]Zhao Y, Liu YF, Duan ZS, et al., 2016a. Distributed average computation for multiple time-varying signals with output measurements. Int J Rob Nonl Contr, 26(13):2899-2915.

[21]Zhao Y, Duan ZS, Wen GH, et al., 2016b. Distributed finite-time tracking of multiple non-identical second-order nonlinear systems with settling time estimation. Automatica, 64:86-93.

[22]Zhao Y, Liu YF, Li ZK, et al., 2017. Distributed average tracking for multiple signals generated by linear dynamical systems: an edge-based framework. Automatica, 75:158-166.

[23]Zhou J, Wen CY, Yang GH, 2014. Adaptive backstepping stabilization of nonlinear uncertain systems with quantized input signal. IEEE Trans Autom Contr, 59(2):460-464.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE