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

WANG Lan; XIE Da; DONG Yiping; CAO Jinde

doi:10.21656/1000-0887.400212

Volume 40 Issue 11

Turn off MathJax

Article Contents

Article Navigation > Applied Mathematics and Mechanics > 2019 > 40(11): 1214-1223

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:

PDF( 1067 KB)

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

doi: 10.21656/1000-0887.400212

¹Department of Fundamental Courses, Wuxi Institute of Technology, Wuxi, Jiangsu 214121, P.R.China;²School of Mathematics, Southeast University, Nanjing 211189, P.R.China;³China Key System & Integrated Circuit Co., Ltd, Wuxi, Jiangsu 214063, P.R.China

Received Date: 2019-07-15
Rev Recd Date: 2019-09-03
Publish Date: 2019-11-01

Abstract

Abstract

A quasi-ARX multilayer learning network prediction model was established and applied to the adaptive control of nonlinear systems. The kernel of the model is an improved neuro-fuzzy network: one part is a 3-layer nonlinear network with an off-line training self-associative network, the other part is a 3-layer neuro-fuzzy network adjusted online. Accordingly, the parameters were classified and the corresponding estimation algorithms were given. Then, the controller design scheme was proposed based on the advantages of the macrostructure of the model. Simulation analysis verifies the effectiveness of the proposed model.
- quasi-ARX model,
- multilayer learning network,
- adaptive control

FullText(HTML)

References(17)

References

[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.