Adaptive Control of Discrete-time Nonlinear Systems Using ITF-ORVFL

Xiaofei Zhang; Hongbin Ma; Wenchao Zuo; Man Luo

doi:10.1109/JAS.2019.1911801

Volume 9 Issue 3

Mar. 2022

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2022 > 9(3): 556-563

Xiaofei Zhang, Hongbin Ma, Wenchao Zuo, and Man Luo, "Adaptive Control of Discrete-time Nonlinear Systems Using ITF-ORVFL," IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 556-563, Mar. 2022. doi: 10.1109/JAS.2019.1911801

Citation:

Xiaofei Zhang, Hongbin Ma, Wenchao Zuo, and Man Luo, "Adaptive Control of Discrete-time Nonlinear Systems Using ITF-ORVFL," IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 556-563, Mar. 2022. doi: 10.1109/JAS.2019.1911801

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Adaptive Control of Discrete-time Nonlinear Systems Using ITF-ORVFL

doi: 10.1109/JAS.2019.1911801

Xiaofei Zhang^{1, 2
,},
Hongbin Ma^{3, 4
,
,},
Wenchao Zuo^{3, 5
,},
Man Luo^{3, 6
,}

1.
School of Automation, Beijing Institute of Technology, Beijing 100081, China
2.
School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
3.
School of Automation, Beijing Institute of Technology, Beijing 100081, China
4.
State Key Laboratory of Intelligent Control and Decision of Complex Systems (Beijing Institute of Technology), Beijing 100081, China
5.
Beijing Institute of Electronic System Engineering, Beijing 100854, China
6.
Ant Group, Beijing 310013, China

Funds: This work was partially supported by the Ministry of Science and Technology of China (2018AAA0101000, 2017YFF0205306, WQ20141100198) and the National Natural Science Foundation of China (91648117)

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Author Bio:
Xiaofei Zhang received the B.S. degree in automation from Hebei United University, in 2014, received the M.S. degree in control engineering from Inner Mongolia University of Science and Technology, in 2016, and received the Ph.D. degree from Beijing Institute of Technology, in 2021. He is currently pursuing the post-doctorate at the School of Vehicle and Mobility, Tsinghua University. His current research interests include adaptive control, machine learning, and safety of the intended functionality

Hongbin Ma received the bachelor degree from Zhengzhou University, in 2001, and received the Ph.D. degree from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences, in 2006. He has been a Professor with Beijing Institute of Technology since 2009. His current research interests include adaptation, learning and recognition, especially adaptive estimation and control, as well as machine vision and swarm intelligence

Wenchao Zuo received the B.S. degree in mechanical and electronic engineering from Hainan University, in 2017, and received the M.S. degree from the School of Automation, Beijing Institute of Technology, in 2020. He is working at Beijing Institute of Electronic System Engineering. His current research interests include hybrid robot and visual servo

Man Luo received the B.S. degree in mechanical design and manufacture and automation major from Huazhong Agricultural University, in 2018, and received the M.S. degree from the School of Automation, BeijingInstitute of Technology, in 2021. She is working at Ant Group, Beijing. Her current research interests include computer vision, deep learning, and object detection
Corresponding author: Hongbin Ma, e-mail: mathmhb@bit.edu.cn
Received Date: 2019-06-23
Revised Date: 2019-08-23
Accepted Date: 2019-10-02

Available Online: 2019-11-12

Abstract

Abstract

Random vector functional ink (RVFL) networks belong to a class of single hidden layer neural networks in which some parameters are randomly selected. Their network structure in which contains the direct links between inputs and outputs isunique, and stability analysis and real-time performance are two difficulties of the control systems based on neural networks. In this paper, combining the advantages of RVFL and the ideas of online sequential extreme learning machine (OS-ELM) and initial-training-free online extreme learning machine (ITF-OELM), a novel online learning algorithm which is named as initial-training-free online random vector functional link algo rithm (ITF-ORVFL) is investigated for training RVFL. The link vector of RVFL network can be analytically determined based on sequentially arriving data by ITF-ORVFL with a high learning speed, and the stability for nonlinear systems based on this learning algorithm is analyzed. The experiment results indicate that the proposed ITF-ORVFL is effective in coping with nonparametric uncertainty.
- Adaptive control,
- initial-training-free online learning algorithm,
- random vector functional link networks

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References(29)

References

[1]	E. Rios-Patron and R. D. Braatz, “On the identification and control of dynamical systems using neural networks,” IEEE Trans. Neural Networks, vol. 8, no. 2, pp. 452–452, Mar. 1997.
[2]	M. A. Halali, V. Azari, M. Arabloo, A. H. Mohammadi, and A. Bahadori, “Application of a radial basis function neural network to estimate pressure gradient in water-oil pipelines,” J. Taiwan Institute of Chemical Engineers, vol. 58, pp. 189–202, Jan. 2016. doi: 10.1016/j.jtice.2015.06.042
[3]	L. Liu, Y. J. Liu, and S. C. Tong, “Fuzzy based multi-error constraint control for switched nonlinear systems and its applications,” IEEE Trans. Fuzzy Systems, 2018.
[4]	L. Liu, Y. J. Liu, and S. C. Tong, “Neural networks-based adaptive finite-time fault-tolerant control for a class of strict-feedback switched nonlinear systems,” IEEE Trans. Cybernetics, vol. 49, no. 7, pp. 2536–2545, Jul. 2019. doi: 10.1109/TCYB.2018.2828308
[5]	T. T. Gao, Y. J. Liu, L. Liu, and D. P. Li, “Adaptive neural networkbased control for a class of nonlinear pure-feedback systems with time-varying full state constraints,” IEEE/CAA. J. Autom. Sinica, vol. 5, no. 5, pp. 923–933, Sep. 2018. doi: 10.1109/JAS.2018.7511195
[6]	G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: A new learning scheme of feedforward neural networks,” Neural Networks, vol. 2, no. 3, pp. 985–990, 2004.
[7]	G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, Dec. 2006. doi: 10.1016/j.neucom.2005.12.126
[8]	N. Y. Liang, G. B. Huang, P. Saratchandran, and N. Sundararajan, “A fast and accurate online sequential learning algorithm for feedforward networks,” IEEE Trans. Neural Networks, vol. 17, no. 6, pp. 1411–1423, 2006. doi: 10.1109/TNN.2006.880583
[9]	H. T. Huynh and Y. Won, “Regularized online sequential learning algorithm for single-hidden layer feed forward neural networks,” Pattern Recognition Letters, vol. 32, no. 14, pp. 1930–1935, 2011. doi: 10.1016/j.patrec.2011.07.016
[10]	X. H. Gao, K. I. Wong, P. K. Wong, and C. M. Vong, “Adaptive control of rapidly time-varying discrete-time system using initial-training-free online extreme learning machine,” Neurocomputing, vol. 194, pp. 117–125, 2016. doi: 10.1016/j.neucom.2016.01.071
[11]	S. Dehuri and S. B. Cho, “A comprehensive survey on functional link neural networks and an adaptive PSO-BP learning for CFLNN,” Neural Computing and Applications, vol. 19, no. 2, pp. 187–205, 2010. doi: 10.1007/s00521-009-0288-5
[12]	Y. H. Pao, Adaptive Pattern Recognition and Neural Networks. Addison-Wesley Publishing, Boston MA USA, 1989.
[13]	Y. H. Pao, S. M. Phillips, and D. J. Sobajic, “Neuralnet computing and the intelligent control of systems,” Int. J. Control, vol. 56, no. 2, pp. 263–289, 2016.
[14]	Y. H. Pao, G. H. Park, and D. J. Sobajic, “Learning and generalization characteristics of the random vector functional-link net,” Neurocomputing, vol. 6, no. 2, pp. 163–180, 1994. doi: 10.1016/0925-2312(94)90053-1
[15]	L. Zhang and P. N. Suganthan, “A comprehensive evaluation of random vector functional link networks,” Information Sciences, vol. 367, pp. 1094–1105, Nov. 2016.
[16]	B. Igelnik and Y. H. Pao, “Stochastic choice of basis functions in adaptive function approximation and the functional-link net,” IEEE Trans. Neural Networks, vol. 6, no. 6, pp. 1320–1329, 1995. doi: 10.1109/72.471375
[17]	C. L. P. Chen, Z. L. Liu, and S. Feng, “Universal approximation capability of broad learning system and its structural variations,” IEEE Trans. Neural Networks and Learning Systems, vol. 30, no. 4, pp. 1191–1204, Apr. 1191.
[18]	P. L. Bartlett, “The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network,” IEEE Trans. Information Theory, vol. 44, no. 2, pp. 525–536, 1998. doi: 10.1109/18.661502
[19]	Y. H. Pao and S. M. Phillips, “The functional link net and learning optimal control,” Neurocomputing, vol. 9, no. 2, pp. 149–164, 1995. doi: 10.1016/0925-2312(95)00066-F
[20]	C. Y. Sun, W. He, W. L. Ge, and C. Chang, “Adaptive neural network control of biped robots,” IEEE Trans. Systems Man Cybernetics-Systems, vol. 47, no. 2, pp. 315–326, Feb. 2017.
[21]	F. C. Chen and H. K. Khalit, “Adaptive control of a class of nonlinear discrete-time systems using neural networks,” IEEE Trans. Autom. Control, vol. 40, no. 5, pp. 791–801, May 1995. doi: 10.1109/9.384214
[22]	D. X. Ba, K. K. Ahn, and N. T. Tai, “Adaptive integraltype neural sliding mode control for pneumatic muscle actuator,” Int. J. Autom. Technology, vol. 8, no. 6, pp. 888–895, 2014. doi: 10.20965/ijat.2014.p0888
[23]	A. H. Jafari, R. Dhaouadi, and A. Jhemi, “Nonlinear friction estimation in elastic drive systems using a dynamic neural network-based observer,” J. Advanced Computational Intelligence and Intelligent Infor-matics, vol. 17, no. 4, pp. 637–646, 2013. doi: 10.20965/jaciii.2013.p0637
[24]	H. J. Liang, Y. Zhou, H. Ma, and Q. Zhou, “Adaptive distributed observer approach for cooperative containment control of nonidentical networks,” IEEE Trans. Systems Man Cybernetics-Systems, vol. 49, no. 2, pp. 299–307, Feb. 2019. doi: 10.1109/TSMC.2018.2791513
[25]	F. J. Luan, J. Na, Y. B. Huang, and G. B. Gao, “Adaptive neural network control for robotic manipulators with guaranteed finite-time convergence,” Neurocomputing, vol. 337, pp. 153–164, Apr. 2019. doi: 10.1016/j.neucom.2019.01.063
[26]	Y. C. Ouyang, L. Dong, L. Xue, and C. Y. Sun, “Adaptive control based on neural networks for an uncertain 2-DOF helicopter system with input deadzone and output constraints,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 807–815, 2019. doi: 10.1109/JAS.2019.1911495
[27]	H. J. Yang and J. K. Liu, “An adaptive RBF neural network control method for a class of nonlinear systems,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 457–462, 2018. doi: 10.1109/JAS.2017.7510820
[28]	X. L. Li, C. Jia, D. X. Liu, and D. W. Ding, “Adaptive control of nonlinear discrete-time systems by using OS-ELM neural networks,” Abstract and Applied Analysis, 2014.
[29]	C. Jia, X. L. Li, K. Wang, and D. W. Ding, “Adaptive control of nonlinear system using online error minimum neural networks,” ISA Trans., vol. 65, pp. 125–132, Nov. 2016. doi: 10.1016/j.isatra.2016.07.012

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A new application of random vector functional link networks in control algorithms
The stability analysis of the control systems based on random vector functional link networks
An online learning algorithm for training random vector functional link networks

Adaptive Control of Discrete-time Nonlinear Systems Using ITF-ORVFL

doi: 10.1109/JAS.2019.1911801

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通讯作者: 陈斌, bchen63@163.com

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