2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

考虑量化输入和输出约束的互联系统自适应分散跟踪控制

秦贞华 何熊熊 李刚 伍益明

秦贞华, 何熊熊, 李刚, 伍益明. 考虑量化输入和输出约束的互联系统自适应分散跟踪控制.自动化学报, 2021, 47(5): 1111-1124 doi: 10.16383/j.aas.c180786
引用本文: 秦贞华, 何熊熊, 李刚, 伍益明. 考虑量化输入和输出约束的互联系统自适应分散跟踪控制.自动化学报, 2021, 47(5): 1111-1124 doi: 10.16383/j.aas.c180786
Qin Zhen-Hua, He Xiong-Xiong, Li Gang, Wu Yi-Ming. Adaptive decentralized tracking control for nonlinear interconnected systems with input quantization and output constraints. Acta Automatica Sinica, 2021, 47(5): 1111-1124 doi: 10.16383/j.aas.c180786
Citation: Qin Zhen-Hua, He Xiong-Xiong, Li Gang, Wu Yi-Ming. Adaptive decentralized tracking control for nonlinear interconnected systems with input quantization and output constraints. Acta Automatica Sinica, 2021, 47(5): 1111-1124 doi: 10.16383/j.aas.c180786

考虑量化输入和输出约束的互联系统自适应分散跟踪控制

doi: 10.16383/j.aas.c180786
基金项目: 

国家自然科学基金 61873239

国家自然科学基金 61803135

国家自然科学基金 61473262

浙江省公益技术应用研究计划项目 LGG18F020015

浙江省公益技术应用研究计划项目 LGF21F020011

详细信息
    作者简介:

    何熊熊  浙江工业大学信息工程学院教授. 1997年获浙江大学工业控制技术研究所博士学位. 主要研究方向为机器人, 迭代学习控制, 智能系统以及信号处理. E-mail: hxx@zjut.edu.cn

    李刚  浙江理工大学数学系讲师. 2013年获哈尔滨工业大学数学系博士学位.主要研究方向为最优化理论及应用. E-mail: ligang@zstu.edu.cn

    伍益明  杭州电子科技大学网络空间安全学院副教授. 2016年获浙江工业大学信息工程学院博士学位. 主要研究方向为智能体系统协同控制, 安全和容侵控制. E-mail: ymwu@hdu.edu.cn

    通讯作者:

    秦贞华  浙江机电职业技术学院讲师. 2012年获浙江工业大学信息学院硕士学位. 现为浙江工业大学信息工程学院在读博士研究生. 主要研究方向为迭代学习控制, 自适应控制, 智能体系统协同控制.本文通信作者. E-mail: qinzhenhua@zime.edu.cn

Adaptive Decentralized Tracking Control for Nonlinear Interconnected Systems With Input Quantization and Output Constraints

Funds: 

National Natural Science Foundation of China 61873239

National Natural Science Foundation of China 61803135

National Natural Science Foundation of China 61473262

Zhejiang Provincial Basic Public Welfare Research Project LGG18F020015

Zhejiang Provincial Basic Public Welfare Research Project LGF21F020011

More Information
    Author Bio:

    HE Xiong-Xiong  Professor at the College of Information Engineering, Zhejiang University of Technology. He received his Ph. D. degree from the Institute of Industrial Control Technology, Zhejiang University, in 1997. His research interest covers robotics, iterative learning control, intelligence systems, and signal processing

    LI Gang  Lecturer at the Department of Mathematics, Zhejiang Sci-Tech University. He received his Ph. D. degree from Harbin Institute of Technology in 2013. His research interest covers optimization theory and application

    WU Yi-Ming  Associate professor at the School of Cyberspace, Hangzhou Dianzi University. He received his Ph. D. degree in control science and engineering from Zhejiang University of Technology, in 2016. His research interest covers multi-agent systems, resilient consensus, and secure control systems

    Corresponding author: QIN Zhen-Hua  Lecturer at the College of Information Engineering, Zhejiang Institute of Mechanical and Electrical Engineering. She graduated from Zhejiang University of Technology in 2012 and she is currently a Ph. D. candidate at the Department of Information Engineering, Zhejiang University of Technology. Her research interest covers iterative learning control, adaptive control, and consensus of multi-agent systems. Corresponding author of this paper
  • 摘要: 本文考虑具有量化输入和输出约束的一类非线性互联系统的自适应分散跟踪控制设计. 分别针对量化参数已知和未知两种情况, 基于反推(Backstepping)设计法, 利用神经网络逼近特性, 设计自适应分散跟踪控制策略. 通过定义新的未知常量和非线性光滑函数, 设计自适应参数估计项来消除未知互联项对系统的影响. 进一步考虑量化参数未知的情形, 引入一个新的不等式来转化输入信号, 并构建新的自适应补偿项来处理量化影响. 同时, 障碍李雅普诺夫函数的引入, 确保了系统输出不违反约束条件. 与现有量化输入设计相比, 本文所提方法不要求未知非线性项满足李普希兹条件, 并且允许量化参数未知. 该设计方法保证了闭环系统所有信号最终一致有界, 而且跟踪误差能够收敛到原点的小邻域内, 同时保证输出不违反约束条件. 最后, 仿真算例验证了所提方法具备良好的跟踪控制性能.
    Recommended by Associate Editor JI Hai-Bo
    1)  本文责任编委 季海波
  • 图  1  三重倒立摆示意图

    Fig.  1  Schematic of tripled inverted pendulums

    图  2  输出$y_1$和$y_{1, r}$的轨迹

    Fig.  2  Trajectories of output $y_1$ and $y_{1, r}$

    图  3  输出$y_2$和$y_{2, r}$的轨迹

    Fig.  3  Trajectories of output $y_2$ and $y_{2, r}$

    图  4  输出$y_3$和$y_{3, r}$的轨迹

    Fig.  4  Trajectories of output $y_3$ and $y_{3, r}$

    图  5  量化参数已知时跟踪误差$z_{1, 1}$的轨迹

    Fig.  5  Trajectory of tracking error $z_{1, 1}$ with known quantization parameters

    图  6  量化参数已知时跟踪误差$z_{2, 1}$的轨迹

    Fig.  6  Trajectory of tracking error $z_{2, 1}$ with known quantization parameters

    图  7  量化参数已知时跟踪误差$z_{3, 1}$的轨迹

    Fig.  7  Trajectory of tracking error $z_{3, 1}$ with known quantization parameters

    图  8  量化参数已知时输入$u_1$和$q_1(u_1)$的轨迹

    Fig.  8  Trajectories of input $u_1$ and $q_1(u_1)$ with known quantization parameters

    图  9  量化参数已知时输入$u_2$和$q_2(u_2)$的轨迹

    Fig.  9  Trajectories of input $u_2$ and $q_2(u_2)$ with known quantization parameters

    图  10  量化参数已知时输入$u_3$和$q_3(u_3)$的轨迹

    Fig.  10  Trajectories of input $u_3$ and $q_3(u_3)$ with known quantization parameters

    图  11  量化参数未知时跟踪误差$z_{1, 1}$的轨迹

    Fig.  11  Trajectory of tracking error $z_{1, 1}$ with unknown quantization parameters

    图  12  量化参数未知时跟踪误差$z_{2, 1}$的轨迹

    Fig.  12  Trajectory of tracking error $z_{2, 1}$ with unknown quantization parameters

    图  13  量化参数未知时跟踪误差$z_{3, 1}$的轨迹

    Fig.  13  Trajectory of tracking error $z_{3, 1}$ with unknown quantization parameters

    图  14  量化参数未知时输入$u_1$和$q_1(u_1)$的轨迹

    Fig.  14  Trajectories of input $u_1$ and $q_1(u_1)$ with unknown quantization parameters

    图  15  量化参数未知时输入$u_2$和$q_2(u_2)$的轨迹

    Fig.  15  Trajectories of input $u_2$ and $q_2(u_2)$ with unknown quantization parameters

    图  16  量化参数未知时输入$u_3$和$q_3(u_3)$的轨迹

    Fig.  16  Trajectories of input $u_3$ and $q_3(u_3)$ with unknown quantization parameters

  • [1] Jain S, Khorrami F. Decentralized adaptive output feedback design for large-scale nonlinear systems. IEEE Transactions on Automatic Control, 1997, 42(5): 729-735 doi: 10.1109/9.580893
    [2] Wen C Y. Decentralized adaptive regulation. IEEE Transactions on Automatic Control, 1994, 39(10): 2163-2166 doi: 10.1109/9.328806
    [3] Spooner J T, Passino K M. Decentralized adaptive control of nonlinear systems using radial basis neural networks. IEEE Transactions on Automatic Control, 1999, 44(11): 2050- 2057 doi: 10.1109/9.802914
    [4] Hua C C, Guan X P, Shi P. Adaptive fuzzy control for uncertain interconnected time-delay systems. Fuzzy Sets and Systems, 2005, 153(3): 447-458 doi: 10.1016/j.fss.2005.01.002
    [5] Mehraeen S, Jagannathan S, Crow M L. Decentralized dynamic surface control of large-scale interconnected systems in strict-feedback form using neural networks with asymptotic stabilization. IEEE Transactions on Neural Networks, 2011, 22(11): 1709-1722 doi: 10.1109/TNN.2011.2140381
    [6] Zhou Q, Shi P, Liu H H, Xu S Y. Neural-network-based decentralized adaptive output-feedback control for large-scale stochastic nonlinear systems. IEEE Transactions on Systems, Man, and Cybernetics --- Part B: Cybernetics, 2012, 42(6): 1608-1619 doi: 10.1109/TSMCB.2012.2196432
    [7] Jiang Z P. Decentralized and adaptive nonlinear tracking of large-scale systems via output feedback. IEEE Transactions on Automatic Control, 2000, 45(11): 2122-2128 doi: 10.1109/9.887638
    [8] Tong S C, Liu C L, Li Y M. Fuzzy adaptive decentralized control for large-scale nonlinear systems with dynamical uncertainties. IEEE Transactions on Fuzzy Systems, 2010, 18(5): 845-861 doi: 10.1109/TFUZZ.2010.2050326
    [9] Zhou J, Wen C Y. Decentralized backstepping adaptive output tracking of interconnected nonlinear systems. IEEE Transactions on Automatic Control, 2008, 53(10): 2378- 2384 doi: 10.1109/TAC.2008.2007524
    [10] Wang C L, Lin Y. Decentralized adaptive tracking control for a class of interconnected nonlinear time-varying systems. Automatica, 2015, 54: 16-24 doi: 10.1016/j.automatica.2015.01.041
    [11] Zhou J, Wen C Y, Wang W. Adaptive control of uncertain nonlinear systems with quantized input signal. Automatica, 2018, 95: 152-162 doi: 10.1016/j.automatica.2018.05.014
    [12] Wang C L, Wen C Y, Lin Y. Decentralized adaptive backstepping control for a class of interconnected nonlinear systems with unknown actuator failures. Journal of the Franklin Institute, 2015, 352(3): 835-850 doi: 10.1016/j.jfranklin.2014.11.019
    [13] Liu Z, Wang F, Zhang Y, Chen C L P. Fuzzy adaptive quantized control for a class of stochastic nonlinear uncertain systems. IEEE Transactions on Cybernetics, 2016, 46(2): 524-534 doi: 10.1109/TCYB.2015.2405616
    [14] Xing L T, Wen C Y, Su H Y, Cai J P, Wang L. A new adaptive control scheme for uncertain nonlinear systems with quantized input signal. Journal of the Franklin Institute, 2015, 352(12): 5599-5610 doi: 10.1016/j.jfranklin.2015.06.020
    [15] Liu T, Jiang Z P, Hill D J. A sector bound approach to feedback control of nonlinear systems with state quantization. Automatica, 2012, 48(1): 145-152 doi: 10.1016/j.automatica.2011.09.041
    [16] Lai G Y, Liu Z, Chen C L P, Zhang Y. Adaptive asymptotic tracking control of uncertain nonlinear system with input quantization. Systems and Control Letters, 2016, 96: 23-29 doi: 10.1016/j.sysconle.2016.06.010
    [17] Tee K P, Ge S S, Tay E H. Barrier Lyapunov functions for the control of output-constrained nonlinear systems. Automatica, 2009, 45(4): 918-927 doi: 10.1016/j.automatica.2008.11.017
    [18] Han S I, Lee J M. Adaptive fuzzy backstepping dynamic surface control for output-constrained non-smooth nonlinear dynamic system. International Journal of Control Automation and Systems, 2012, 10(4): 684-696 doi: 10.1007/s12555-012-0403-8
    [19] He W, Zhang S, Ge S S. Adaptive control of a flexible crane system with the boundary output constraint. IEEE Transactions on Industrial Electronics, 2014, 61(8): 4126-4133 doi: 10.1109/TIE.2013.2288200
    [20] Sanner R M, Slotine J E. Gaussian networks for direct adaptive control. IEEE Transactions on Neural Networks, 1992, (3): 837-863
    [21] Li K W, Tong S C, Li Y M. Finite-time adaptive fuzzy decentralized control for nonstrict-feedback nonlinear systems with output-constraint. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018. DOI: 10.1109/TSMC.2018.2870698
    [22] Hua C C, Zhang L L, Guan X P. Distributed adaptive neural network output tracking of leader-following high-order stochastic nonlinear multiagent systems with unknown dead-zone input. IEEE Transactions on Cybernetics, 2017, 47(1): 177-185 doi: 10.1109/TCYB.2015.2509482
    [23] Yoo S J, Park J B, Choi, Y H. Decentralized adaptiv stabilization of interconnected nonlinear systems with unknown non-symmetric dead-zone inputs. Automatica, 2009, 45: 436-443 doi: 10.1016/j.automatica.2008.07.019
  • 加载中
图(16)
计量
  • 文章访问数:  916
  • HTML全文浏览量:  139
  • PDF下载量:  204
  • 被引次数: 0
出版历程
  • 收稿日期:  2018-11-26
  • 录用日期:  2019-04-07
  • 刊出日期:  2021-05-21

目录

    /

    返回文章
    返回