Asynchronous Learning-Based Output Feedback Sliding Mode Control for Semi-Markov Jump Systems: A Descriptor Approach

Zheng Wu; Yiyun Zhao; Fanbiao Li; Tao Yang; Yang Shi; Weihua Gui

doi:10.1109/JAS.2024.124416

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 11.8, Top 4% (SCI Q1)

CiteScore: 17.6, Top 3% (Q1)
Google Scholar h5-index: 77， TOP 5

Turn off MathJax

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2024 > In Press, Accepted Manuscript

Z. Wu, Y. Zhao, F. Li, T. Yang, Y. Shi, and W. Gui, “Asynchronous learning-based output feedback sliding mode control for semi-Markov jump systems: A descriptor approach,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1358–1369, Jun. 2024. doi: 10.1109/JAS.2024.124416

Citation:

Z. Wu, Y. Zhao, F. Li, T. Yang, Y. Shi, and W. Gui, “Asynchronous learning-based output feedback sliding mode control for semi-Markov jump systems: A descriptor approach,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1358–1369, Jun. 2024. doi: 10.1109/JAS.2024.124416

Citation:

PDF( 1782 KB)

Asynchronous Learning-Based Output Feedback Sliding Mode Control for Semi-Markov Jump Systems: A Descriptor Approach

doi: 10.1109/JAS.2024.124416

Funds: This work was supported in part by the National Science Fund for Excellent Young Scholars of China (62222317), the National Science Foundation of China (62303492), the Major Science and Technology Projects in Hunan Province (2021GK1030), the Science and Technology Innovation Program of Hunan Province (2022WZ1001); the Key Research and Development Program of Hunan Province (2023GK2023), and the Fundamental Research Funds for the Central Universities of Central South University (2024ZZTS0116)

More Information

Author Bio:
Zheng Wu received the B.S. degree in automation from Hebei University of Technology in 2018, and the M.S. degree in control science and engineering from Central South University in 2021. He is now a Ph.D. candidate in control science and engineering at Central South University. His research interests include sliding mode control, fuzzy logic systems and semi-Markovian jump systems

Yiyun Zhao received the B.E. and Ph.D. degrees in electrical engineering from Northwestern Polytechnical University in 2017 and 2024, respectively. In 2024, he joined the Central South University, as a Lecturer. His research interests include more electric aircraft, nonlinear control, and mechatronic servo control

Fanbiao Li (Senior Member, IEEE) received the B.S. degree in applied mathematics from Mudanjiang Normal University in 2008, the M.S. degree in operational research and cybernetics from Heilongjiang University in 2012, and the Ph.D. degree in control theory and control engineering from the Harbin Institute of Technology in 2015. From December 2013 to April 2015, he was a Joint Training Ph.D. Student with the School of Electrical and Electronic Engineering, The University of Adelaide, Australia. From April 2015 to February 2016, he was a Research Associate with the School of Electrical and Electronic Engineering, The University of Adelaide, Australia. From July 2016 to June 2020, he was an Associate Professor with Central South University. From April 2017 to March 2018, he was an Alexander von Humboldt Research Fellow with the University of Duisburge-Essen, Germany. He is currently a Full Professor with Central South University. His research interests include control and design of aircraft brake system, sliding-mode control, and fault diagnosis and identification. Prof. Li currently serves as an Associate Editor for a number of journals, including IEEE Transactions on System, Man, and Cybernetics: Systems, IEEE Transactions on Fuzzy Systems, IEEE/CAA Journal of Automatica Sinica, and Cognitive Computation

Tao Yang (Senior Member, IEEE) received the M.Eng. degree in control theory & engineering from Shanghai Jiao Tong University, Shanghai in 2008, and the Ph.D. degree in electrical engineering from the University of Nottingham, UK in 2013. Since 2013, he has been a Researcher with the Power Electronics, Machines and Control Group, University of Nottingham, UK, where he became an Assistant Professor in 2016, and an Associate Professor in 2019. His research interests include high-speed electric motor drive control, power electronic conversion, electrical system design, and optimization for more electric/hybrid/all-electric aircraft applications. His Ph.D. research within EU Clean Sky on Modeling electrical power system for more-electric aircraft applications has resulted in him winning the inaugural Clean Sky Best Ph.D. Award in 2016. He is an Associate Editor for the IEEE Transactions on Transportation Electrification and Chinese Journal of Aeronautics

Yang Shi (Fellow, IEEE) received the B.S. and Ph.D. degrees in mechanical engineering and automatic control from Northwestern Polytechnical University in 1994 and 1998, respectively, and the Ph.D. degree in electrical and computer engineering from the University of Alberta, Canada in 2005. He was a Research Associate with the Department of Automation, Tsinghua University from 1998 to 2000. From 2005 to 2009, he was an Assistant Professor and an Associate Professor with the Department of Mechanical Engineering, University of Saskatchewan, Canada. In 2009, he was with the University of Victoria, Canada, and he is currently a Professor with the Department of Mechanical Engineering, University of Victoria, Canada. His current research interests include networked and distributed systems, model predictive control, cyber-physical systems, robotics and mechatronics, navigation and control of autonomous systems, and energy system applications. Dr. Shi was a recipient of the University of Saskatchewan Student Union Teaching Excellence Award in 2007, and the Faculty of Engineering Teaching Excellence Award in 2012 at the University of Victoria (UVic). He is the recipient of the JSPS Invitation Fellowship (short-term) in 2013, the UVic Craigdarroch Silver Medal for Excellence in Research in 2015, the 2017 IEEE Transactions on Fuzzy Systems Outstanding Paper Award, the Humboldt Research Fellowship for Experienced Researchers in 2018; CSME Mechatronics Medal in 2023; and the IEEE Dr.-Ing Eugene Mittelmann Achievement Award in 2023. He is IFAC Council Member; a VP on Conference Activities of IEEE IES and the Chair of IEEE IES Technical Committee on Industrial Cyber-Physical Systems. He is currently a Co-Editor-in-Chief of IEEE Canadian Journal of Electrical and Computer Engineering. He also serves as an Associate Editor for Automatica, IEEE Transactions on Automatic Control, Annual Review in Controls, etc. He is a Distinguished Lecturer of IEEE Industrial Electronics Society. He is a Fellow of ASME, CSME, Engineering Institute of Canada (EIC), Canadian Academy of Engineering (CAE), and a Registered Professional Engineer in British Columbia, Canada

Weihua Gui (Member, IEEE) received the B.E. degree in electrical engineering and the M.E. degree in automatic control engineering from Central South University in 1976 and 1981, respectively. From 1986 to 1988 he was a Visiting Scholar at Universität-GH-Duisburg, Germany. Since 1991, he has been a Full Professor at Central South University. Since 2013, he has been an Academician with the Chinese Academy of Engineering. His main research interests include modeling and optimal control of complex industrial process, distributed robust control, and fault diagnoses
Corresponding author: Yiyun Zhao, e-mail: zhaoyiyun@mail.nwpu.edu.cn
Received Date: 2024-01-24
Revised Date: 2024-02-23
Accepted Date: 2024-03-13

Available Online: 2024-04-16

Abstract

Abstract

This paper presents an asynchronous output-feedback control strategy of semi-Markovian systems via sliding mode-based learning technique. Compared with most literature results that require exact prior knowledge of system state and mode information, an asynchronous output-feedback sliding surface is adopted in the case of incompletely available state and non-synchronization phenomenon. The holonomic dynamics of the sliding mode are characterized by a descriptor system in which the switching surface is regarded as the fast subsystem and the system dynamics are viewed as the slow subsystem. Based upon the co-occurrence of two subsystems, the sufficient stochastic admissibility criterion of the holonomic dynamics is derived by utilizing the characteristics of cumulative distribution functions. Furthermore, a recursive learning controller is formulated to guarantee the reachability of the sliding manifold and realize the chattering reduction of the asynchronous switching and sliding motion. Finally, the proposed theoretical method is substantiated through two numerical simulations with the practical continuous stirred tank reactor and F-404 aircraft engine model, respectively.
- Asynchronous switching,
- learning-based control,
- output feedback,
- semi-Markovian jump systems,
- sliding mode control (SMC)

FullText(HTML)

References(41)

References

[1]	M. V. Basin, P. Yu, and Y. B. Shtessel, “Hypersonic missile adaptive sliding mode control using finite-time and fixed-time observers,” IEEE Trans. Ind. Electrons., vol. 23, no. 1, pp. 57–67, 2018.
[2]	M. V. Basin, P. C. R. Ramirez, and F. Guerra-Avellaneda, “Continuous fixed-time controller design for mechatronic systems with incomplete measurements,” IEEE/ASME Trans. Mechatronics, vol. 23, no. 1, pp. 57–67, 2018. doi: 10.1109/TMECH.2017.2700459
[3]	X. Su, Y. Wen, P. Shi, S. Wang, and W. Assawinchaichote, “Event-triggered fuzzy control for nonlinear systems via sliding mode approach,” IEEE Trans. Fuzzy Syst., vol. 29, no. 2, pp. 336–344, 2021. doi: 10.1109/TFUZZ.2019.2952798
[4]	J. Liu, L. Wu, C. Wu, W. Luo, and L. G. Franquelo, “Event-triggering dissipative control of switched stochastic systems via sliding mode,” Automatica, vol. 103, pp. 261–273, 2019. doi: 10.1016/j.automatica.2019.01.029
[5]	Y. Wei, J. H. Park, J. Qiu, L. Wu, and H. Jung, “Sliding mode control for semi-Markovian jump systems via output feedback,” Automatica, vol. 21, pp. 133–141, 2017.
[6]	L. Wu, J. Liu, S. Vazquez, and S. K. Mazumder, “Sliding mode control in power converters and drives: A review,” IEEE/CAA J. Autom. Sinica., vol. 9, no. 3, pp. 392–406, 2022. doi: 10.1109/JAS.2021.1004380
[7]	J. Li, Y. Niu, and J. Song, “Sliding mode control design under multiple nodes round-robin-like protocol and packet length-dependent lossy network,” Automatica, vol. 134, p. 109942, 2021. doi: 10.1016/j.automatica.2021.109942
[8]	L. Ovalle, H. Ríos, M. Llama, and L. Fridman, “Continuous sliding-mode output-feedback control for stabilization of a class of underactuated systems,” IEEE Trans. Autom. Control, vol. 67, no. 2, pp. 986–992, 2022. doi: 10.1109/TAC.2021.3075179
[9]	V. Utkin, “Discussion aspects of high-order sliding mode control,” IEEE Trans. Autom. Control, vol. 61, no. 3, pp. 829–833, 2016. doi: 10.1109/TAC.2015.2450571
[10]	Y. Liu, H. Li, R. Lu, Z. Zuo, and X. Li, “An overview of finite/fixed-time control and its application in engineering systems,” IEEE/CAA J. Autom. Sinica., vol. 9, no. 12, pp. 2106–2120, 2022. doi: 10.1109/JAS.2022.105413
[11]	A. Mustafa, N. K. Dhar, and N. K. Verma, “Event-triggered sliding mode control for trajectory tracking of nonlinear systems,” IEEE/CAA J. Autom. Sinica., vol. 7, no. 1, pp. 307–314, 2020. doi: 10.1109/JAS.2019.1911654
[12]	Z. Cao, Y. Niu, and C. Peng, “Finite-time stabilization of uncertain Markovian jump systems: An adaptive gain-scheduling control method,” IEEE Trans. Autom. Control, 2023. doi: 10.1109/TAC.2023.3307951,2023
[13]	Z. Zhong, X. Wang, and H. K. Lam, “Finite-time fuzzy sliding mode control for nonlinear descriptor systems,” IEEE/CAA J. Autom. Sinica., vol. 8, no. 6, pp. 1141–1152, 2021. doi: 10.1109/JAS.2021.1004024
[14]	A. Rosales, Y. Shtessel, L. Fridman, and C. B. Panathula, “Chattering analysis of HOSM controlled systems: Frequency domain approach,” IEEE Trans. Autom. Control, vol. 62, no. 8, pp. 4109–4115, 2017. doi: 10.1109/TAC.2016.2619559
[15]	M. Liu, L. Zhang, P. Shi, and Y. Zhao, “Fault estimation sliding mode observer with digital communication constraints,” IEEE Trans. Autom. Control, vol. 63, no. 10, pp. 3434–3441, 2018. doi: 10.1109/TAC.2018.2794826
[16]	G. P. Incremona, M. Rubagotti, M. Tanelli, and A. Ferrara, “A general framework for switched and variable gain higher order sliding mode control,” IEEE Trans. Autom. Control, vol. 66, no. 4, pp. 1718–1724, 2021. doi: 10.1109/TAC.2020.2996423
[17]	F. Li, Z. Wu, C. Yang, Y. Shi, T. Huang, and W. Gui, “A novel learning-based asynchronous sliding mode control for discrete-time semi-Markov jump systems,” Automatica, vol. 143, p. 110428, 2022. doi: 10.1016/j.automatica.2022.110428
[18]	Z. Man, S. Khoo, X. Yu and J. Jin, “A new sliding mode-based learning control scheme,” in Proc. 6th IEEE Conf. Industrial Electronics and Applications, 2011, pp. 1906–1911.
[19]	D. M. Tuan, Z. Man, C. Zhang, and J. Jin, “A new sliding mode-based learning control for uncertain discrete-time systems,” in Proc. 12th Int. Conf. Control Automation Robotics & Vision, 2012, pp. 741–746.
[20]	X. Hu, C. Hu, X. Si, and Y. Zhao, “Robust sliding mode-based learning control for MIMO nonlinear nonminimum phase system in general form,” IEEE Trans. Cybern., vol. 49, no. 10, pp. 3793–3805, 2019. doi: 10.1109/TCYB.2018.2874682
[21]	L. Liu, L. Ma, J. Zhang, and Y. Bo, “Sliding mode control for nonlinear Markovian jump systems under denial-of-service attacks,” IEEE/CAA J. Autom. Sinica., vol. 7, no. 6, pp. 1638–1648, 2020. doi: 10.1109/JAS.2019.1911531
[22]	O. L. V. Costa, M. D. Fragoso, and M. G. Todorov, “A detector-based approach for the H₂ control of Markov jump linear systems with partial information,” IEEE Trans. Autom. Control, vol. 60, no. 5, pp. 1219–1234, 2015. doi: 10.1109/TAC.2014.2366253
[23]	J. Song, Y. Niu, and Y. Zou, “Asynchronous sliding mode control of Markovian jump systems with time-varying delays and partly accessible mode detection probabilities,” Automatica, vol. 93, pp. 33–41, 2018. doi: 10.1016/j.automatica.2018.03.037
[24]	X. Su, C. Wang, H. Chang, Y. Yang, and W. Assawinchaichote, “Event-triggered sliding mode control of networked control systems with Markovian jump parameters,” Automatica, vol. 125, p. 109405, 2021. doi: 10.1016/j.automatica.2020.109405
[25]	F. Li, C. Du, C. Yang, and W. Gui, “Passivity-based asynchronous sliding mode control for delayed singular Markovian jump systems,” IEEE Trans. Autom. Control, vol. 63, no. 8, pp. 2715–2721, 2018. doi: 10.1109/TAC.2017.2776747
[26]	F. Li, C. Du, C. Yang, L. Wu, and W. Gui, “Finite-time asynchronous sliding mode control for Markovian jump systems,” Automatica, vol. 109, p. 108503, 2019. doi: 10.1016/j.automatica.2019.108503
[27]	V. Dragan and E. F. Costa, “Optimal stationary dynamic output-feedback controllers for discrete-time linear systems with Markovian jumping parameters and additive white noise perturbations,” IEEE Trans. Autom. Control, vol. 61, no. 12, pp. 3912–3924, 2016. doi: 10.1109/TAC.2016.2529505
[28]	G. Ciardo, R. A. Marie, B. Sericola, and K. S. Trivedi, “Performability analysis using semi-Markov reward processes,” IEEE Trans. Comput., vol. 39, no. 10, pp. 1251–1264, 2017.
[29]	S. Al-Dahidi, F. D. Maio, P. Baraldi, and E. Zio, “Remaining useful life estimation in heterogeneous fleets working under variable operating conditions,” Reliab. Eng. Syst. Saf., vol. 156, pp. 109–124, 2016. doi: 10.1016/j.ress.2016.07.019
[30]	L. Zhang, T. Yang, and P. Colaneri, “Stability and stabilization of semi-Markov jump linear systems with exponentially modulated periodic distributions of sojourn time,” IEEE Trans. Autom. Control, vol. 62, no. 6, pp. 2870–2885, 2017. doi: 10.1109/TAC.2016.2618844
[31]	Z. Cao, Y. Niu, and Y. Zou, “Adaptive neural sliding mode control for singular semi-Markovian jump systems against actuator attacks,” IEEE Trans. Sys. Man. Cybern. Syst., vol. 51, no. 3, pp. 1523–1533, 2021.
[32]	J. Huang and Y. Shi, “Stochastic stability and robust stabilization of semi-Markov jump linear systems,” Int. J. Robust. Nonlinear Control, vol. 23, no. 18, pp. 2028–2043, 2013. doi: 10.1002/rnc.2862
[33]	L. Zhang, Y. Leng, and P. Colaneri, “Stability and stabilization of discrete-time semi-Markov jump linear systems via semi-Markov kernel approach,” IEEE Trans. Autom. Control, vol. 61, no. 2, pp. 503–508, 2016.
[34]	M. Souza, M. De Almeida, A. R. Fioravanti, and O. L. V. Costa, “ $ {\cal{H}}_2$ output-feedback cluster control forcontinuous semi-markov jump linear systems with erlang dwell times,” IEEE Control Syst. Lett., vol. 7, pp. 109–114, 2023. doi: 10.1109/LCSYS.2022.3185976
[35]	Z. Wu, P. Shi, Z. Shu, H. Su, and R. Lu, “Passivity-based asynchronous control for Markov jump systems,” IEEE Trans. Autom. Control, vol. 62, no. 4, pp. 2020–2025, 2017. doi: 10.1109/TAC.2016.2593742
[36]	B. Saporta, and E. F. Costa, “Approximate Kalman-Bucy filter for continuous-time semi-Markov jump linear systems,” IEEE Trans. Autom. Control, vol. 61, no. 8, pp. 2035–2048, 2016. doi: 10.1109/TAC.2015.2495578
[37]	S. Xu and J. Lam, “Robust control and filtering of singular systems,” Berlin, Germany: Springer, 2006.
[38]	J. Zhou, J. H. Park, and Q. Kong, “Robust resilient L₂ – L_∞ control for uncertain stochastic systems with multiple time delays via dynamic output feedback,” J. Franklin Inst., vol. 353, no. 13, pp. 3078–3103, 2016. doi: 10.1016/j.jfranklin.2016.06.004
[39]	Z. Li, D. Shen, and X. Yu, “Enhancing iterative learning control with fractional power update law,” IEEE/CAA J. Autom. Sinica., vol. 10, no. 5, pp. 1137–1149, 2023. doi: 10.1109/JAS.2023.123525
[40]	F. Li, X. Cao, C. Zhou, and C. Yang, “Event-triggered asynchronous sliding mode control of CSTR based on Markov model,” J. Franklin Inst., vol. 358, pp. 4687–4704, 2021. doi: 10.1016/j.jfranklin.2021.04.007
[41]	M. Fang, H. Kodamana, and B. Huang, “Real-time mode diagnosis for processes with multiple operating conditions using switching conditional random fields,” IEEE Trans. Ind. Electron., vol. 67, no. 6, pp. 5060–5070, 2020. doi: 10.1109/TIE.2019.2924876

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(13)

Get Citation

PDF

XML

Article Metrics

Article views (52) PDF downloads(17)

Asynchronous Learning-Based Output Feedback Sliding Mode Control for Semi-Markov Jump Systems: A Descriptor Approach

doi: 10.1109/JAS.2024.124416

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Export File

Citation

Format

Content