Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks

Haowei Lin; Bo Zhao; Derong Liu; Cesare Alippi

doi:10.1109/JAS.2020.1003225

Volume 7 Issue 4

Jun. 2020

IEEE/CAA Journal of Automatica Sinica

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

CiteScore: 23.5, Top 2% (Q1)
Google Scholar h5-index: 77， TOP 5

Turn off MathJax

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2020 > 7(4): 954-964

Haowei Lin, Bo Zhao, Derong Liu and Cesare Alippi, "Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 954-964, July 2020. doi: 10.1109/JAS.2020.1003225

Citation:

Haowei Lin, Bo Zhao, Derong Liu and Cesare Alippi, "Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 954-964, July 2020. doi: 10.1109/JAS.2020.1003225

Citation:

PDF( 1621 KB)

Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks

doi: 10.1109/JAS.2020.1003225

Funds: This work was supported in part by the National Natural Science Foundation of China (61533017, 61973330, 61773075, 61603387), the Early Career Development Award of SKLMCCS (20180201), and the State Key Laboratory of Synthetical Automation for Process Industries (2019-KF-23-03)

More Information

Author Bio:
Haowei Lin received the B.S. degree at the School of Automation, Guangdong Polytechnic Normal University, in 2017. He is currently pursuing the M.S. degree at the School of Automation, Guangdong University of Technology. His research interests include adaptive dynamic programming and fault tolerant control

Bo Zhao (M’16) received the B.S. degree in automation, and Ph.D. degree in control science and engineering, all from Jilin University, in 2009 and 2014, respectively. He was a Post-Doctoral Fellow with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, from 2014 to 2017. Then, he joined the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, from 2017 to 2018. He is currently an Associate Professor with the School of Systems Science, Beijing Normal University, Beijing, China. He has authored or coauthored over 80 journal and conference papers. His research interests include adaptive dynamic programming, robot control, fault diagnosis and tolerant control, optimal control, and artificial intelligence-based control. He was the secretary of Adaptive Dynamic Programming and Reinforcement Learning Professional Committee of Chinese Automation Association (CAA), and the secretary of 2017 the 24th International Conference on Neural Information Processing. He is IEEE Member, Asian-Pacific Neural Network Society (APNNS) Member and CAA Member

Derong Liu (S’91−M’94−SM’96−F’05) received the Ph.D. degree in electrical engineering from the University of Notre Dame, Notre Dame, IN, USA, in 1994. He was a Staff Fellow with General Motors Research and Development Center, Warren, MI, USA, from 1993 to 1995. He was an Assistant Professor with the Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA, from 1995 to 1999. He joined the University of Illinois at Chicago, Chicago, IL, USA, in 1999, and became a Full Professor of Electrical and Computer Engineering, and of Computer Science, in 2006. He served as the Associate Director of the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing, China, from 2010 to 2015. He is currently a Full Professor with the School of Automation, Guangdong University of Technology, Guangzhou, China. He has authored or co-authored 19 books. Prof. Liu was a recipient of the Michael J. Birck Fellowship from the University of Notre Dame, in 1990, the Harvey N. Davis Distinguished Teaching Award from the Stevens Institute of Technology, in 1997, the Faculty Early Career Development Award from the National Science Foundation in 1999, the University Scholar Award from the University of Illinois, from 2006 to 2009, and the Overseas Outstanding Young Scholar Award from the National Natural Science Foundation of China in 2008. He was the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems from 2010 to 2015. He is the Editor-in-Chief of Artificial Intelligence Review (Springer). He is a Fellow of the International Neural Network Society and the International Association of Pattern Recognition

Cesare Alippi (F’06) is a Professor with the Politecnico di Milano, Milano, Italy and Università della Svizzera Italiana, Lugano, Switzerland. Alippi is a visiting professor at the Guangdong University of Technology in Guangzhou, China and Consultant Professor at the Northwestern Polytechnical Univerisity in Xi’an, China. He has been a Visiting Researcher/Professor at UCL (UK), MIT (USA), ESPCI (F), CASIA (PRC), A*STAR (SIN), UKobe (JP). Alippi is an IEEE Fellow, Member of the Administrative Committee of the IEEE Computational Intelligence Society, Board of Governors member of the International Neural Network Society, Board of Directors member of the European Neural Network Society, Past Vice-President education of the IEEE Computational Intelligence Society, past associate editor of the IEEE Transactions on Emerging Topics in Computational Intelligence, the IEEE Computational Intelligence Magazine, theIEEE Transactions on Instrumentation and Measurements, the IEEE Transactions on Neural Networks (and Learning Systems). He received the 2018 IEEE CIS Outstanding Computational Intelligence Magazine Award, the 2016 Gabor Award from the International Neural Networks Society and the IEEE Computational Intelligence Society Outstanding Transactions on Neural Networks and Learning Systems Paper Award; in 2013 the IBM Faculty award; in 2004 the IEEE Instrumentation and Measurement Society Young Engineer Award. Current research activity addresses Adaptation and Learning in Non-stationary Environments, Graph Learning and Intelligence for Embedded, IoT and Cyber-physical Systems. He holds 8 patents, has published one monograph book, 7 edited books and about 200 papers in international journals and conference proceedings
Corresponding author: B. Zhao is with the School of Systems Science, Beijing Normal University, Beijing 100875, China (e-mail: zhaobo@bnu.edu.cn)
Received Date: 2020-03-03
Revised Date: 2020-04-18
Accepted Date: 2020-05-13

Available Online: 2020-06-11

Abstract

Abstract

In this paper, a data-based fault tolerant control (FTC) scheme is investigated for unknown continuous-time (CT) affine nonlinear systems with actuator faults. First, a neural network (NN) identifier based on particle swarm optimization (PSO) is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network (PSOCNN) is employed to solve the Hamilton-Jacobi-Bellman equation (HJBE) more efficiently. Then, a data-based FTC scheme, which consists of the NN identifier and the fault compensator, is proposed to achieve actuator fault tolerance. The stability of the closed-loop system under actuator faults is guaranteed by the Lyapunov stability theorem. Finally, simulations are provided to demonstrate the effectiveness of the developed method.
- Adaptive dynamic programming (ADP),
- critic neural network,
- data-based,
- fault tolerant control (FTC),
- particle swarm optimization (PSO)

FullText(HTML)

References(51)

References

[1]	F. L. Lewis, D. Vrabie, and V. L. Syrmos, Optimal Control. New York: John Wiley & Sons, 2012.
[2]	P. J. Werbos, “Approximate dynamic programming for real-time control and neural modeling,” in Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches, D. A. White and D. A. Sofge, Eds. New York, NY, USA: Van Nostrand Reinhold, 1992, ch. 13.
[3]	J. J. Murray, C. J. Cox, G. G. Lendaris, and R. Saeks, “Adaptive dynamic programming,” IEEE Trans. Syst.,Man,Cybern.,Part C:Appl. Rev., vol. 32, no. 2, pp. 140–153, Dec. 2002. doi: 10.1109/TSMCC.2002.801727
[4]	D. R. Liu, D. Wang, D. B. Zhao, Q. L. Wei, and N. Jin, “Neural-network-based optimal control for a class of unknown discrete-time nonlinear systems using globalized dual heuristic programming,” IEEE Trans. Autom. Sci. Eng., vol. 9, no. 3, pp. 628–634, Jul. 2012. doi: 10.1109/TASE.2012.2198057
[5]	H. Jiang and H. G. Zhang, “Iterative ADP learning algorithms for discretetime multi-player games,” Artificial Intelligence Review, vol. 50, no. 1, pp. 75–91, Jan. 2018. doi: 10.1007/s10462-017-9603-1
[6]	J. Valasek, J. Doebbler, M. D. Tandale, and A. J. Meade, “Improved adaptivereinforcement learning control for morphing unmanned air vehicles,” IEEE Trans. Syst.,Man,Cybern.,Part B:Cybern., vol. 38, no. 4, pp. 1014–1020, Aug. 2008. doi: 10.1109/TSMCB.2008.922018
[7]	F. L. Lewis and D. Vrabie, “Reinforcement learning and adaptive dynamic programming for feedback control,” IEEE Circuits and Syst. Mag., vol. 9, no. 3, pp. 32–50, Aug. 2009. doi: 10.1109/MCAS.2009.933854
[8]	F. C. Meng and Y. P. Dai, “Reinforcement learning adaptive control for upper limb rehabilitation robot based on fuzzy neural network,” in Proc. 31st Chinese Control Conf., Hefei, China, Jul. 2012, pp. 5157–5161.
[9]	D. R. Liu, X. Yang, D. Wang, and Q. L. Wei, “Reinforcement-learning-based robust controller design for continuous-time uncertain nonlinear systems subject to input constraints,” IEEE Trans. Cybern., vol. 45, no. 7, pp. 1372–1385, Apr. 2015. doi: 10.1109/TCYB.2015.2417170
[10]	D. Wang, D. R. Liu, and H. L. Li, “Policy iteration algorithm for online design of robust control for a class of continuous-time nonlinear systems,” IEEE Trans. Autom. Sci. Eng., vol. 11, no. 2, pp. 627–632, Apr. 2014. doi: 10.1109/TASE.2013.2296206
[11]	D. R. Liu and Q. L. Wei, “Policy iteration adaptive dynamic programming algorithm for discrete-time nonlinear systems,” IEEE Trans. Neural Netw. Learn. Syst., vol. 25, no. 3, pp. 621–634, Sep. 2014. doi: 10.1109/TNNLS.2013.2281663
[12]	A. Al-Tamimi, F. L. Lewis, and M. Abu-Khalaf, “Discrete-time nonlinear HJB solution using approximate dynamic programming: Convergence proof,” IEEE Trans. Syst.,Man,Cybern.,Part B:Cybern., vol. 38, no. 4, pp. 943–949, Jun. 2008. doi: 10.1109/TSMCB.2008.926614
[13]	Y. H. Zhu and D. B. Zhao, “Comprehensive comparison of online ADP algorithms for continuous-time optimal control,” Artificial Intelligence Review, vol. 49, no. 4, pp. 531–547, Feb. 2018. doi: 10.1007/s10462-017-9548-4
[14]	D. R. Liu, Q. L. Wei, D. Wang, X. Yang, and H. L. Li, Adaptive Dynamic Programming with Applications in Optimal Control. Cham, Switzerland: Springer, 2017.
[15]	B. Kiumarsi, K. G. Vamvoudakis, H. Modares, and F. L. Lewis, “Optimal and autonomous control using reinforcement learning: A survey,” IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 6, pp. 2042–2062, Dec. 2018. doi: 10.1109/TNNLS.2017.2773458
[16]	D. R. Liu, Y. C. Xu, Q. L. Wei, and X. L. Liu, “Residential energy scheduling for variable weather solar energy based on adaptive dynamic programming,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 36–46, 2018.
[17]	Q. L. Wei, D. R. Liu, Y. Liu, and R. Z. Song, “Optimal constrained self-learning battery sequential management in microgrid via adaptive dynamic programming,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 168–176, 2017. doi: 10.1109/JAS.2016.7510262
[18]	B. Zhao and D. R. Liu, “Event-triggered decentralized tracking control of modular reconfigurable robots through adaptive dynamic programming,” IEEE Trans. Ind. Electron., vol. 67, no. 4, pp. 3054–3064, Apr. 2020. doi: 10.1109/TIE.2019.2914571
[19]	S. C. Tong, B. Y. Huo, and Y. M. Li, “Observer-based adaptive decentralized fuzzy fault-tolerant control of nonlinear large-scale systems with actuator failures,” IEEE Trans. Fuzzy Syst., vol. 22, no. 1, pp. 1–15, Jan. 2013.
[20]	Y. M. Li, K. K. Sun, and S. C. Tong, “Adaptive fuzzy robust fault-tolerant optimal control for nonlinear large-scale systems,” IEEE Trans. Fuzzy Syst., vol. 26, no. 5, pp. 2899–2914, Dec. 2017.
[21]	Y. M. Li, K. K. Sun, and S. C. Tong, “Observer-based adaptive fuzzy fault-tolerant optimal control for SISO nonlinear systems,” IEEE Trans. Cybern., vol. 49, no. 2, pp. 649–661, Jan. 2018.
[22]	B. Zhao, D. R. Liu, and Y. C. Li, “Online fault compensation control based on policy iteration algorithm for a class of affine non-linear systems with actuator failures,” IET Control Theory Appl., vol. 10, no. 15, pp. 1816–1823, Oct. 2016. doi: 10.1049/iet-cta.2015.1105
[23]	B. Zhao, D. R. Liu, and Y. C. Li, “Observer based adaptive dynamic programming for fault tolerant control of a class of nonlinear systems,” Inform. Sci., vol. 384, no. 1, pp. 21–33, Apr. 2017.
[24]	X. Wu, S. J. Zhang, and W. F. Shuang, “Optimal adaptive tracking control for a class of MIMO uncertain nonlinear systems with actuator failures,” in Proc. 36th Chinese Control Conf., Dalian, China, Sep. 2017, pp. 6974–6979.
[25]	L. Liu, Z. S. Wang, and H. G. Zhang, “Adaptive fault-tolerant tracking control for MIMO discrete-time systems via reinforcement learning algorithm with less learning parameters,” IEEE Trans. Autom. Sci. Eng., vol. 14, no. 1, pp. 299–313, Jan. 2016.
[26]	Z. S. Wang, L. Liu, H. G. Zhang, and G. Y. Xiao, “Fault-tolerant controller design for a class of nonlinear MIMO discrete-time systems via online reinforcement learning algorithm,” IEEE Trans. Syst.,Man,Cybern.,Syst., vol. 46, no. 5, pp. 611–622, May 2016. doi: 10.1109/TSMC.2015.2478885
[27]	Z. S. Wang, L. Liu, and H. G. Zhang, “Neural network-based model-free adaptive fault-tolerant control for discrete-time nonlinear systems with sensor fault,” IEEE Trans. Syst.,Man,Cybern.,Syst., vol. 47, no. 8, pp. 2351–2362, Aug. 2017. doi: 10.1109/TSMC.2017.2672664
[28]	H. G. Zhang, K. Zhang, Y. L. Cai, and J. Han, “Adaptive fuzzy fault-tolerant tracking control for partially unknown systems with actuator faults via integral reinforcement learning method,” IEEE Trans. Fuzzy Syst., vol. 27, no. 10, pp. 1986–1998, Jan. 2019. doi: 10.1109/TFUZZ.2019.2893211
[29]	J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. IEEE Int. Conf. Neural Network, Perth, Australia, Dec. 1995, pp. 1942–1948.
[30]	Y. H. Shi and R. Eberhart, “A modified particle swarm optimizer,” in Proc. IEEE Int. Conf. Evolutionary Computation, Anchorage, AK, USA: IEEE, May 1998, pp. 69–73.
[31]	M. Clerc and J. Kennedy, “The particle swarm: Explosion, stability and convergence in multi-dimensional complex space,” IEEE Trans. Evolut. Comput., vol. 20, no. 1, pp. 1671–1676, Aug. 2002.
[32]	S. Martin and M. Choi, “Nonlinear electrical impedance tomography reconstruction using artificial neural networks and particle swarm optimization,” IEEE Trans. Magnetics, vol. 52, no. 3, pp. 1–4, Oct. 2015.
[33]	G. Das, P. K. Pattnaik, and S. K. Padhy, “Artificial neural network trained by particle swarm optimization for non-linear channel equalization,” Expert Systems with Applications, vol. 41, no. 7, pp. 3491–3496, Jun. 2014. doi: 10.1016/j.eswa.2013.10.053
[34]	K. Y. Chan, T. Dillon, E. Chang, and J. Singh, “Prediction of short-term traffic variables using intelligent swarm-based neural networks,” IEEE Trans. Contr. Syst. Technol., vol. 21, no. 1, pp. 263–274, Jan. 2013. doi: 10.1109/TCST.2011.2180386
[35]	Z. W. Zheng, L. Sun, and L. H. Xie, “Error-constrained LOS path following of a surface vessel with actuator saturation and faults,” IEEE Trans. Syst.,Man,Cybern.,Syst., vol. 48, no. 10, pp. 1794–1805, Jul. 2017.
[36]	B. Jiang, M. Staroswiecki, and V. Cocquempot, “Fault accommodation for nonlinear dynamic systems,” IEEE Trans. Autom. Contr., vol. 51, no. 9, pp. 1578–1583, Sep. 2006. doi: 10.1109/TAC.2006.878732
[37]	D. R. Liu and Q. L. Wei, “Policy iteration adaptive dynamic programming algorithm for discrete-time nonlinear systems,” IEEE Trans. Neural Netw. Learn. Syst., vol. 25, no. 3, pp. 621–634, Sep. 2013.
[38]	B. Zhao, D. Wang, G. Shi, D. R. Liu, and Y. C. Li, “Decentralized control for large-scale nonlinear systems with unknown mismatched interconnections via policy iteration,” IEEE Trans. Syst.,Man,Cybern.,Syst., vol. 48, no. 10, pp. 1725–1735, Oct. 2017.
[39]	M. Jiang, Y. P. Luo, and S. Y. Yang, “Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm,” Information Processing Letters, vol. 102, no. 1, pp. 8–16, Apr. 2007. doi: 10.1016/j.ipl.2006.10.005
[40]	W. Yu, Recent Advances in Intelligent Control Systems. London, UK: Springer-Verlag, 2009.
[41]	X. Yang, D. R. Liu, and D. Wang, “Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints,” Int. J. Control, vol. 87, no. 3, pp. 553–566, Apr. 2014. doi: 10.1080/00207179.2013.848292
[42]	X. N. Zhong and H. B. He, “An event-triggered ADP control approach for continuous-time system with unknown internal states,” IEEE Trans. Cybernetics, vol. 47, no. 3, pp. 683–694, Apr. 2017. doi: 10.1109/TCYB.2016.2523878
[43]	K. G. Vamvoudakis and F. L. Lewis, “Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem,” Automatica, vol. 46, no. 5, pp. 878–888, May 2010. doi: 10.1016/j.automatica.2010.02.018
[44]	D. Wang, D. R. Liu, Q. C. Zhang, and D. B. Zhao, “Data-based adaptive critic designs for nonlinear robust optimal control with uncertain dynamics,” IEEE Trans. Syst.,Man,Cybern.,Syst., vol. 46, no. 11, pp. 1–12, Oct. 2015.
[45]	X. Yang, D. R. Liu, and Y. Z. Huang, “Neural-network-based online optimal control for uncertain non-linear continuous-time systems with control constraints,” IET Control Theory Appl., vol. 7, no. 17, pp. 2037–2047, Nov. 2013. doi: 10.1049/iet-cta.2013.0472
[46]	B. Zhao, D. R. Liu, and C. Luo, “Reinforcement learning-based optimal stabilization for unknown nonlinear systems subject to inputs with uncertain constraints,” IEEE Trans. Neural Netw. Learn. Syst., 2019. doi: 10.1109/TNNLS.2019.2954983.
[47]	D. R. Liu, H. L. Li, and D. Wang, “Online synchronous approximate optimal learning algorithm for multi-player non-zero-sum games with unknown dynamics,” IEEE Trans. Syst.,Man,Cybern.,Syst., vol. 44, no. 8, pp. 1015–1027, Jan. 2014. doi: 10.1109/TSMC.2013.2295351
[48]	Y. Sokolov, R. Kozma, L. D. Werbos, and P. J. Werbos, “Complete stability analysis of a heuristic approximate dynamic programming control design,” Automatica, vol. 59, pp. 9–18, Sep. 2015. doi: 10.1016/j.automatica.2015.06.001
[49]	D. R. Liu, D. Wang, F.-Y. Wang, H. L. Li, and X. Yang, “Neural-networkbased online HJB solution for optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems,” IEEE Trans. Cybern., vol. 44, no. 12, pp. 2834–2847, Sep. 2014. doi: 10.1109/TCYB.2014.2357896
[50]	B. Zhao, D. R. Liu, and C. Alippi, “Sliding mode surface-based approximate optimal control for uncertain nonlinear systems with asymptotically stable critic structure,” IEEE Trans. Cybern., Jan. 2020. doi: 10.1109/TCYB.2019.2962011.
[51]	D. B. Zhao, J. Q. Yi, and D. R. Liu, “Particle swarn optimized adaptive dynamic programming,” in Proc. IEEE Int. Symp. Approx. Dyn. Programm. Reinforcement Learn., Honolulu, HI, USA: IEEE, Apr. 2007, pp. 32–37.

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(11) / Tables(1)

Get Citation

PDF

XML

Article Metrics

Article views (1797) PDF downloads(121)

Highlights

A data-based fault tolerant control scheme is investigated.
The unknown system dynamics is approximated by PSO-NN identifier.
The HJB equation is solved with a high successful rate by the PSOCNN.
The online fault tolerant control is shown to be optimal.

Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks

doi: 10.1109/JAS.2020.1003225

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Highlights

Export File

Citation

Format

Content