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基于准ARX多层学习网络模型的非线性系统自适应控制

王兰 谢达 董宜平 曹进德

王兰, 谢达, 董宜平, 曹进德. 基于准ARX多层学习网络模型的非线性系统自适应控制[J]. 应用数学和力学, 2019, 40(11): 1214-1223. doi: 10.21656/1000-0887.400212
引用本文: 王兰, 谢达, 董宜平, 曹进德. 基于准ARX多层学习网络模型的非线性系统自适应控制[J]. 应用数学和力学, 2019, 40(11): 1214-1223. doi: 10.21656/1000-0887.400212
WANG Lan, XIE Da, DONG Yiping, CAO Jinde. Adaptive Control of Nonlinear Systems Based on Quasi-ARX Multilayer Learning Network Models[J]. Applied Mathematics and Mechanics, 2019, 40(11): 1214-1223. doi: 10.21656/1000-0887.400212
Citation: WANG Lan, XIE Da, DONG Yiping, CAO Jinde. Adaptive Control of Nonlinear Systems Based on Quasi-ARX Multilayer Learning Network Models[J]. Applied Mathematics and Mechanics, 2019, 40(11): 1214-1223. doi: 10.21656/1000-0887.400212

基于准ARX多层学习网络模型的非线性系统自适应控制

doi: 10.21656/1000-0887.400212
基金项目: 江苏省高等学校自然科学研究项目(19KJB120013);江苏高校“青蓝工程”优秀青年骨干教师项目(014000773/2018-00376);江苏省政策引导类计划(国际科技合作)(BZ2018031);江苏省高等职业院校教师专业带头人高端研修资助项目(2019GRGDYX129)
详细信息
    作者简介:

    王兰(1983—),女,副教授,博士(E-mail: wanglan@wxit.edu.cn);曹进德(1963—),男,教授,博士,博士生导师(通讯作者. E-mail: jindecao@seu.edu.cn).

  • 中图分类号: O231.2

Adaptive Control of Nonlinear Systems Based on Quasi-ARX Multilayer Learning Network Models

  • 摘要: 建立了准ARX多层学习网络预测模型,并用于非线性系统自适应控制问题.该模型的内核部分为一个改进的神经模糊网络(NFNs):一部分为三层非线性网络结构,采用自联想网络进行离线训练;另一部分为三层NFNs,采取在线调整.据此对参数进行分类,给出相应调整算法. 然后,基于模型宏观结构的优势给出控制器设计方案.仿真分析给出该建模方法的有效性.
  • [1] NO L J P, KERSCHEN G. Nonlinear system identification in structural dynamics: 10 more years of progress[J].Mechanical Systems and Signal Processing,2017,〖STHZ〗 83: 2-35.
    [2] 芦泽阳, 李树江, 王向东. 采用RBF网络的喷雾机喷杆自适应动态面跟踪控制[J]. 应用数学和力学, 2019,40(7): 801-809.(LU Zeyang, LI Shujiang, WANG Xiangdong. Adaptive RBF-network dynamic surface tracking control of sprayer boom systems[J]. Applied Mathematics and Mechanics,2019,40(7): 801-809.(in Chinese))
    [3] SUTRISNO I, JAMI’IN M A, HU J, et al. A self-organizing quasi-linear ARX RBFN model for nonlinear dynamical systems identification[J]. SICE Journal of Control, Measurement, and System Integration,2016,9(2): 70-77.
    [4] LJUNG L. System Identification: Theory for the User [M]. 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 1999.
    [5] HU J, KUMAMARU K, HIRASAWA K. A quasi-ARMAX approach to modelling of non-linear systems[J]. International Journal of Control,2001,74(18): 1754-1766.
    [6] NARENDRA K S, PARTHASARATHY K. Identification and control of dynamical systems using neural networks[J]. IEEE Transactions on Neural Networks,1990,1(1): 4-27.
    [7] YOUNG P C, MCKENNA P, BRUUN J. Identification of non-linear stochastic systems by state dependent parameter estimation[J]. International Journal of Control,2001,74(18): 1837-1857.
    [8] WANG L, CHENG Y, HU J L. Stabilizing switching control for nonlinear system based on quasi-ARX RBFN model[J]. IEEJ Transactions on Electrical and Electronic Engineering,2012,7(4): 390-396.
    [9] JANOT A, YOUNG P C, GAUTIER M. Identification and control of electro-mechanical systems using state-dependent parameter estimation[J]. International Journal of Control,2017,〖STHZ〗 90(4): 643-660.
    [10] XU W, PENG H, ZENG X, et al. Deep belief network-based AR model for nonlinear time series forecasting[J]. Applied Soft Computing,2019,77: 605-621.
    [11] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science,2006,313(5786): 504-507.
    [12] WANG L, CHENG Y, HU J, et al. Nonlinear system identification using quasi-ARX RBFN models with a parameter-classified scheme[J]. Complexity,2017,2017: 1-12.
    [13] SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research,2014,15(1): 1929-1958.
    [14] KUREMOTO T, KIMURA S, KOBAYASHI K, et al. Time series forecasting using a deep belief network with restricted Boltzmann machines[J]. Neurocomputing,2014,137: 47-56.
    [15] SCHMIDHUBER J. Deep learning in neural networks: an overview[J]. Neural Networks,2015,61: 85-117.
    [16] LI D, KANG T, HU J, et al. Quasi-linear recurrent neural network based identification and predictive control[C]//2018 International Joint Conference on Neural Networks (IJCNN) . 2018: 1-6.
    [17] HU J, HIRASAWA K, KUMAMARU K. Adaptive predictor for control of nonlinear systems based on neurofuzzy models[C]//1999 European Control Conference (ECC) . Karlsruhe, Germany, 1999.
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出版历程
  • 收稿日期:  2019-07-15
  • 修回日期:  2019-09-03
  • 刊出日期:  2019-11-01

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