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一种新的数据驱动的非线性自适应切换控制方法

牛宏 陶金梅 张亚军

牛宏, 陶金梅, 张亚军. 一种新的数据驱动的非线性自适应切换控制方法. 自动化学报, 2020, 46(11): 2359−2366 doi: 10.16383/j.aas.c190674
引用本文: 牛宏, 陶金梅, 张亚军. 一种新的数据驱动的非线性自适应切换控制方法. 自动化学报, 2020, 46(11): 2359−2366 doi: 10.16383/j.aas.c190674
Niu Hong, Tao Jin-Mei, Zhang Ya-Jun. A new nonlinear adaptive switching control method based on data driven. Acta Automatica Sinica, 2020, 46(11): 2359−2366 doi: 10.16383/j.aas.c190674
Citation: Niu Hong, Tao Jin-Mei, Zhang Ya-Jun. A new nonlinear adaptive switching control method based on data driven. Acta Automatica Sinica, 2020, 46(11): 2359−2366 doi: 10.16383/j.aas.c190674

一种新的数据驱动的非线性自适应切换控制方法

doi: 10.16383/j.aas.c190674
基金项目: 国家自然科学基金(61773107, 61866021, 61991402, 61890924, 61833004, 61973202) and CSC (201808210410)资助
详细信息
    作者简介:

    牛宏:辽宁石油化工大学讲师. 2012年获得东北大学博士学位. 主要研究方向为非线性系统的自适应控制和变结构控制.E-mail: niuhong@lnpu.edu.cn

    陶金梅:辽宁石油化工大学硕士研究生. 主要研究方向为非线性自适应控制, 系统辨识, 数据建模. E-mail: tao_jinmei@hotmail.com

    张亚军:东北大学副教授. 主要研究方向为非线性模糊自适应控制理论, 广义预测控制, 多模型切换控制, 智能解耦控制, 数据驱动控制, 智能控制系统的大数据建模, 工业过程大数据建模及其应用. 本文通信作者. E-mail: yajunzhang@mail.neu.edu.cn

A New Nonlinear Adaptive Switching Control Method Based on Data Driven

Funds: Supported by National Natural Science Foundation of China (61773107, 61866021, 61991402, 61890924, 61833004, 61973202) and CSC (201808210410)
  • 摘要: 针对一类非线性离散时间动态系统, 提出了一种新的非线性自适应切换控制方法. 该方法首先把非线性项分解为前一拍可测部分与未知增量和的形式, 并充分利用被控对象的大数据信息和知识, 把非线性项前一拍可测数据与未知增量都用于控制器设计, 分别设计了线性自适应控制器, 带有非线性项前一拍可测数据补偿的非线性自适应控制器以及带有非线性项未知增量估计与补偿的非线性自适应控制器. 三个自适应控制器通过切换函数和切换规则来协调控制被控对象. 既保证了闭环系统的稳定性, 同时又提高了闭环系统的性能. 分析了闭环切换系统的稳定性和收敛性. 最后, 通过水箱液位系统的物理实验, 实验结果验证了所提算法的有效性.
  • 图  1  带有$v[{{x}}(k)]$前一拍数据及其未知增量补偿的非线性控制器

    Fig.  1  Nonlinear controller with $v[{{x}}(k)]$ previous step data and its unknown incremental compensation

    图  2  切换控制结构

    Fig.  2  Switching control structure

    图  3  水箱液位控制系统图

    Fig.  3  Diagram of tank level control system

    图  6  切换序列

    Fig.  6  Switching sequence

    图  4  采用本文方法时水箱液位的实际响应曲线(输出y)

    Fig.  4  The actual response curve of tank level by the proposed method (output y)

    图  5  采用本文切换控制方法时水箱液位的控制输入u

    Fig.  5  The actual input of tank level by the proposed method in this paper (input u)

  • [1] Lainiotis D G. Optimal adaptive estimation structure and parameter adaption. IEEE Transactions on Automatic Control, 1971, 16(2): 160−170 doi: 10.1109/TAC.1971.1099684
    [2] Narendra K S, Parthasarathy K. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Network, 1990, 1(1): 4−27 doi: 10.1109/72.80202
    [3] Narendra K S, Balakrishnan J. Improving transient response of adaptive control systems using multiple models and switching. IEEE Transactions on Automatic Control, 1994, 39(9): 1861−1866 doi: 10.1109/9.317113
    [4] Narendra K S, Balakrishnan J. Adaptive control using multiple models. IEEE Transactions on Automatic Control, 1997, 42(2): 171−187 doi: 10.1109/9.554398
    [5] Narendra K S, Cheng X. Adaptive control of discrete-time systems using multiple models. IEEE Transactions on Automatic Control, 2000, 45(9): 1669−1686 doi: 10.1109/9.880617
    [6] Chen L J, Narendra K S. Nonlinear adaptive control using neural networks and multiple models. Automatic, 2001, 37(8): 1245−1255 doi: 10.1016/S0005-1098(01)00072-3
    [7] Fu Y, Chai T Y. Nonlinear multivariable adaptive control using multiple models and neural networks. Automatic, 2007, 43(8): 1101−1110
    [8] 石宇静, 柴天佑. 基于神经网络与多模型的非线性自适应广义预测控制. 自动化学报, 2007, 33(5): 540−545

    Shi Yu-Jing, Chai Tian-You. Neural networks and multiple models based nonlinear adaptive generalized predictive control. Acta Automatica Sinica, 2007, 33(5): 540−545
    [9] 柴天佑, 张亚军. 基于非线性项补偿的非线性自适应切换控制方法. 自动化学报, 2010, 37(7): 773−786

    Chai Tian-You, Zhang Ya-Jun. Nonlinear adaptive switching control method based on un-modeled dynamics compensation. Acta Automatica Sinica, 2010, 37(7): 773−786
    [10] Chai T Y, Zhai L F, Yue H. Multiple models and neural networks based decoupling control of ball mill coal-pulverizing systems. Journal of Process Control, 2011, 21(3): 351−366 doi: 10.1016/j.jprocont.2010.11.007
    [11] Wang Y, Chai T Y, Fu Y, Sun J, Wang H. Adaptive decoupling switching control of the forced-circulation evaporation system using neural networks. IEEE Transactions on Control Systems Technology, 2013, 21(3): 964−974 doi: 10.1109/TCST.2012.2193883
    [12] 董泽, 尹二新, 韩璞. 基于迟延估计与Kalman状态跟踪的热工过程动态数据驱动建模. 动力工程学报, 2018, 38(3): 203−210

    Dong Ze, Yin Er-Xin, Han Pu. Dynamic data driven modeling for thermal processes based on delay estimation and Kalman state tracking. Journal of Chinese Society of Power Engineering, 2018, 38(3): 203−210
    [13] Hou Z S and Xu J X. On data-driven control theory: The state of the art and perspective. Acta Automatica Sinica, 2009, 35(6): 650−667 doi: 10.3724/SP.J.1004.2009.00650
    [14] Hou Z S, Wang Z. From model-based control to data-driven control: Survey, classification and perspective. Information Science, 2013, 235: 3−35 doi: 10.1016/j.ins.2012.07.014
    [15] Ma Y J, Zhao S Y, Huang B. Multiple-model state estimation based on variational bayesian inference. IEEE Transactions on Automatic Control, 2018, 64(4): 1679−1685
    [16] 张亚军, 柴天佑, 杨杰. 一类非线性离散时间动态系统的交替辨识算法及应用. 自动化学报, 2017, 43(1): 101−113

    Zhang Ya-Jun, Chai Tian-You, Yang Jie. Alternating identi?cation algorithm and its application to a class of nonlinear discrete-time dynamical systems. Acta Automatica Sinica, 2017, 43(1): 101−113
    [17] He W, Meng T, He X, Ge S S. Unified iterative learning control for flexible structures with input constraints. Automatica, 2018, 96: 326−336 doi: 10.1016/j.automatica.2018.06.051
    [18] Dornheim J, Link N, Gumbsch P. Model-free adaptive optimal control of sequential manufacturing processes using reinforcement learning. arXiv preprint arXiv, 2018, 1809.06646.
    [19] Ma Y J, Zhao S Y, Huang B. Feature extraction of constrained dynamic latent variables. IEEE Transactions on Industrial Informatics, 2019, DOI: 10.1109/TII.2019.2901934.
    [20] Na J, Li G, Wang B, Herrmann G, Zhan S. Robust optimal control of wave energy converters based on adaptive dynamic programming. IEEE Transactions on Sustainable Energy, 2019, 10(2): 961−970 doi: 10.1109/TSTE.2018.2856802
    [21] Chai T Y, Zhang Y J, Wang H, Su C Y, Sun J. Data based virtual un-modeled dynamics driven multivariable nonlinear adaptive switching control. IEEE Transactions on Neural Network, 2011, 22(12): 2154−2172 doi: 10.1109/TNN.2011.2167685
    [22] Tong S C, Sui S, Li Y M. Observed-based adaptive fuzzy tracking control for switched nonlinear systems with dead-zone. IEEE Transactions on Cybernetics, 2015, 45(12): 2816−2826 doi: 10.1109/TCYB.2014.2386912
    [23] Tong S C, Li Y M. Adaptive fuzzy output feedback control for switched nonlinear systems with unmodeled dynamics. IEEE Transactions on Cybernetics, 2017, 47(2): 295−305
    [24] Li Y M, Sui S, Tong S C. Adaptive fuzzy control design for stochastic nonlinear switched systems with arbitrary switchings and unmodeled dynamics. IEEE Transactions on Cybernetics, 2017, 47(2): 403−414
    [25] Ma R C and Zhao J. Backstepping design for global stabilization of switched nonlinear systems in lower triangular form under arbitrary switchings. Automatica, 2010, 46(11): 1819−1823 doi: 10.1016/j.automatica.2010.06.050
    [26] Liu Y J, Gao Y, Tong S C, Li Y M. Fuzzy approximation-based adaptive backstepping optimal control for a class of nonlinear discretetime systems with dead-zone. IEEE Transactions on Fuzzy Systems, 2016, 24(1): 16−28 doi: 10.1109/TFUZZ.2015.2418000
    [27] Long L J, Zhao J. Adaptive output-feedback neural control of switched uncertain nonlinear systems with average dwell time. IEEE Transactions on Neural Network and Learning Systems, 2015, 26(7): 1350−1362 doi: 10.1109/TNNLS.2014.2341242
    [28] Sun K K, Mou S H, Qiu J B, Wang T, Gao H J. Adaptive fuzzy control for nontriangular structural stochastic switched nonlinear systems with full state constraints. IEEE Transactions on Fuzzy Systems, 2019, 27(8): 1587−1601 doi: 10.1109/TFUZZ.2018.2883374
    [29] Qiu J B, Sun K K, Imre J Rudas, Gao H J. Command filter-based adaptive NN control for MIMO nonlinear systems with full-state constraints and actuator hysteresis. IEEE Transactions on Cybernetics, 2019, 99:1-11. DOI: 10.1109/TCYB.2019.2944761
    [30] Goodwin G C, Ramadge P J, Caines P E. Discrete-time multivariable adaptive control. IEEE Transactions on Automatic Control, 1980, 25(3): 449−456
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出版历程
  • 收稿日期:  2019-09-23
  • 录用日期:  2020-01-09
  • 刊出日期:  2020-11-24

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