Volume 43 Issue 11
Nov.  2022
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WANG Lan, DONG Yiping, CAO Jinde. Switching Control of Nonlinear Systems Based on the Quasi-ARX Model and the SVR Algorithm[J]. Applied Mathematics and Mechanics, 2022, 43(11): 1281-1287. doi: 10.21656/1000-0887.430122
Citation: WANG Lan, DONG Yiping, CAO Jinde. Switching Control of Nonlinear Systems Based on the Quasi-ARX Model and the SVR Algorithm[J]. Applied Mathematics and Mechanics, 2022, 43(11): 1281-1287. doi: 10.21656/1000-0887.430122

Switching Control of Nonlinear Systems Based on the Quasi-ARX Model and the SVR Algorithm

doi: 10.21656/1000-0887.430122
  • Received Date: 2022-04-07
  • Rev Recd Date: 2022-04-29
  • Available Online: 2022-11-08
  • Publish Date: 2022-11-30
  • A nonlinear switching control method was proposed based on an improved quasilinear autoregressive with exogenous inputs (quasi-ARX) radial basis function (RBF) network model and the support vector regression (SVR) algorithm. The RBF network was chosen as the nonlinear part of the improved quasi-ARX prediction model. The proposed controller design method was divided into 3 steps: firstly, the nonlinear parameters of the model were determined with the clustering method; secondly, the linear SVR algorithm was used to solve the robustness problem of the control system; thirdly, the switching criterion function was given based on the control error, and the control sequence were determined according to the switching law. Finally, a numerical example was given to verify the effectiveness of the proposed method.

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  • [1]
    HU J, HIRASAWA K, KUMAMARU K. A neurofuzzy-based adaptive predictor for control of nonlinear systems[J]. Transactions of the Society of Instrument and Control Engineers, 1999, 35(8): 1060-1068.
    [2]
    赵玮, 任凤丽. 基于自适应控制的四元数时滞神经网络的有限时间同步[J]. 应用数学和力学, 2022, 43(1): 94-103

    ZHAO Wei, REN Fengli. Finite time adaptive synchronization of quaternion-value neural networks with time delays[J]. Applied Mathematics and Mechanics, 2022, 43(1): 94-103.(in Chinese)
    [3]
    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.
    [4]
    XI Y G, LI D W, LIN S. Model predictive control-status and challenges[J]. Acta Automatica Sinica, 2013, 39(3): 222-236.
    [5]
    王兰, 谢达, 董宜平, 等. 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

    WANG Lan, XIE Da, DONG Yiping, et al. Adaptive control of nonlinear systems based on quasi-ARX multilayer learning network models[J]. Applied Mathematics and Mechanics, 2019, 40(11): 1214-1223.(in Chinese)
    [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]
    LI D P, LIU Y J, TONG S C, et al. Neural networks-based adaptive control for nonlinear state constrained systems with input delay[J]. IEEE Transactions on Cybernetics, 2019, 49(4): 1249-1258.
    [8]
    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.
    [9]
    WU J, SUN W, SU S F, et al. Neural-based adaptive control for nonlinear systems with quantized input and the output constraint[J]. Applied Mathematics and Computation, 2022, 413: 126637.
    [10]
    BILLINGS S A, WEI H L. A new class of wavelet networks for nonlinear system identification[J]. IEEE Transactions on Neural Networks, 2005, 16(4): 862-874.
    [11]
    WANG L, CHENG Y, HU J. 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.
    [12]
    CHENG L, WANG Z, JIANG F, et al. Adaptive neural network control of nonlinear systems with unknown dynamics[J]. Advances in Space Research, 2021, 67(3): 1114-1123.
    [13]
    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, 90(4): 643-660.
    [14]
    YANG D, LI X, QIU J. Output tracking control of delayed switched systems via state-dependent switching and dynamic output feedback[J]. Nonlinear Analysis: Hybrid Systems, 2019, 32: 294-305.
    [15]
    BECHLIOULIS C P, ROVITHAKIS G A. Prescribed performance adaptive control for multi-input multi-output affine in the control nonlinear systems[J]. IEEE Transactions on Automatic Control, 2010, 55(5): 1220-1226.
    [16]
    AWAD M, KHANNA R. Support Vector Regression[M]//Efficient Learning Machines. Berkeley: Apress, 2015: 67-80.
    [17]
    TOIVONEN H T, TÖTTERMAN S, ÅKESSON B M. Identification of state-dependent parameter models with support vector regression[J]. International Journal of Control, 2007, 80(9): 1454-1470.
    [18]
    GOOGWIN G C, SIN K S. Adaptive Filtering Prediction and Control[M]. Dover Publications, 1984.
    [19]
    WANG L, CHENG Y, HU J. A quasi-ARX neural network with switching mechanism to adaptive control of nonlinear systems[J]. SICE Journal of Control, Measurement, and System Integration, 2010, 3(4): 246-252.
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