Dr. Saravanakumar Ramasamy
Contact Information:
Postal address: Shenzhen MSU-BIT University, 1 International University Park Road, Longgang District, 518172 Shenzhen, Guangdong Province, P.R. China.
Office: Room 331, Main Building.
Email: saravana@smbu.edu.cn
Educational Background：
·2013.01 – 2016.02, Ph.D. in Mathematics, Department of Mathematics,
Thiruvalluvar University, Vellore, India., Supervisor: Dr. M. Syed Ali
·2010.07 – 2012.05, M.Sc. in Mathematics, Department of Mathematics, Gandhigram Rural Institute – Deemed to be University, Dindigul, India
·2009.08 – 2010.06, B.Ed. in Mathematics, Lakshmi College of Education - Affiliated to Tamilnadu Teachers Education University, Dindigul, India
·2006.06 – 2009.04, B.Sc. in Mathematics, SRKV College of Arts and Science, Affiliated to Bharathiar University, Coimbatore, India
Working Experience：
·2023.11 – Present, Senior Lecturer, Joint Research Centre on Computational Mathematics and Control, Shenzhen MSU-BIT University.
·2020.04 – 2023.03, Assistant Professor, Graduate School of Advanced Science and Engineering, Hiroshima University, Japan.
·2018.11 – 2020.03, JSPS Postdoctoral Fellow, Graduate School of Engineering, Hiroshima University, Japan.
·2017.01 – 2018.10, Post-doctoral Research Fellow, Research Center for Wind Energy Systems, Kunsan National University, South Korea.
·2016.02 – 2016.10, Visiting Research Fellow, Department of Mathematics, Maejo University, Thailand.
Fundings:
·2016.02 – 2017.01, National Research Council of Thailand, Title: Studies on stability and controller synthesis of delayed stochastic neural networks.
·2018.11 – 2020.03, Japan Society for Promotion of Science, JSPS Research Fellow grant, Title: A New Development of Consensus Control for Multi-Agent System Based on Dynamic Game Theory.
·2020.10 – 2023.04, Japan Society for Promotion of Science, Title: Consensus Control of Networked Multi-Agent Systems and Its Applications.
Research interests：
·Fuzzy modeling and control for nonlinear dynamical systems, Stability analysis of delayed neural networks, Stabilization of Wind turbine control systems.
Publications and Preprints in SCI (E) journals：
[1]A. Kazemy, R. Saravanakumar, Event-Triggered Networked Cascade Control Systems Design Subject to Hybrid Attacks, Mathematical Methods in the Applied Sciences, 2023, accepted (to appear).
[2]R. Saravanakumar, Y.H. Joo, Network-based Robust Exponential Fuzzy Control for Uncertain Systems, Mathematical Methods in the Applied Sciences, 2022, DOI: 10.1002/mma.8943
[3]R. Saravanakumar, H. Dutta, State estimation of memristor-based stochastic neural networks with mixed variable delays, Miskolc Mathematical Notes, 2022, accepted (to appear).
[4]R Datta, R Saravanakumar, R Dey, B Bhattacharya, Further results on stability analysis of Takagi–Sugeno fuzzy time-delay systems via improved Lyapunov–Krasovskii functional, AIMS Mathematics 7 (9), 16464-16481, 2022
[5]R. Saravanakumar, A. Kazemy, Y. Cao, Robust Reliable H∞ Control for Offshore Steel Jacket Platforms via Memory Sampled-data Strategy, Mathematical Methods in the Applied Sciences,2022, DOI:10.1002/mma.8390
[6]R. Saravanakumar, Y. Cao, A. Kazemy, Q. Zhu, Sampled-data based extended dissipative synchronization of stochastic complex dynamical networks, Discrete & Continuous Dynamical Systems-S, vol. 15(11), pp. 3313-3330, 2022
[7]R. Saravanakumar, M. Syed Ali, Extended dissipative criteria for generalized Markovian jump neural networks including asynchronous mode-dependent delayed states, Neural Processing Letters, vol. 54, pp. 1623–1645, 2022.
[8]R. Saravanakumar, R Datta, Y. Cao, New insights on fuzzy sampled-data stabilization of delayed nonlinear systems, Chaos, Solitons & Fractals, Vol. 154, ID: 111654, 2022.
[9]R. Saravanakumar, A Amini, R Datta, Y. Cao, Reliable Memory Sampled-Data Consensus of Multi-agent Systems with Nonlinear Actuator Faults, IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69 (4), pp. 2201 – 2205, 2022.
[10]R. Saravanakumar, Improved stabilization criteria for fuzzy chaotic systems using memory sampled-data strategy, IEEE Control Systems Letters, Vol. 6, pp. 1952 – 1957, 2021, (ESCI indexed).
[11]R. Vadivel, P. Hammachukiattikul, N. Gunasekaran, R. Saravanakumar, H. Dutta, Strict dissipativity synchronization for delayed static neural networks: An event-triggered scheme, Chaos, Solitons & Fractals, Vol. 150, pp. 111212, 2021.
[12]R. Datta, R. Saravanakumar, R. Dey, B. Bhattacharya, and CK Ahn, Improved stabilization criteria for Takagi–Sugeno fuzzy systems with variable delays, Information sciences, Vol. 579, pp. 591-606, 2021.
[13]Ali Kazemy, R. Saravanakumar, and James Lam, Master-slave synchronization of neural networks subject to mixed-type communication attacks, Information Sciences, Vol. 560, pp. 20-34, 2021.
[14]R. Saravanakumar, H. Mukaidani, and P. Muthukumar, Extended dissipative state estimation of delayed stochastic neural networks, Neurocomputing, Vol. 406, pp. 244-252, 2020.
[15]H. Mukaidani, R. Saravanakumar, and Hua Xu, Robust incentive Stackelberg strategy for Markov jump linear stochastic systems via static output feedback, IET Control Theory & Applications, 14(9), 1246-1254. 2020
[16]R. Datta, R. Dey, B. Bhattacharya, R. Saravanakumar, O.M. Kwon, Stability and Stabilization of T–S Fuzzy Systems with Variable Delays via New Bessel–Legendre Polynomial Based Relaxed Integral Inequality, Information Sciences, Vol. 522, pp. 99-123, 2020.
[17]H. Mukaidani, R. Saravanakumar, Hua Xu and W Zhuang, Stackelberg strategy for uncertain Markov jump delay stochastic systems, IEEE Control Systems Letters, Vol. 4 no. 4, pp. 1006-1011, 2020, (ESCI indexed).
[18]R. Datta, R. Dey, B. Bhattacharya, R. Saravanakumar, Tsung-Chih Lin, New delay-range-dependent stability condition for fuzzy Hopfield neural networks via Wirtinger inequality, Journal of Intelligent & Fuzzy Systems, Vol. 38 (5), pp. 6099-6109, 2020.
[19]N. Gunasekaran, R. Saravanakumar, M. Syed Ali and Q. Zhu, Exponential Sampled-data control for T-S Fuzzy Systems: Application to Chua’s circuit, International Journal of Systems Science, Vol. 50, no.16, pp. 2979-2992, 2019.
[20]R. Saravanakumar and Y. H. Joo, Fuzzy dissipative and observer control for wind generator systems: a fuzzy time-dependent LKF approach, Nonlinear Dynamics, Vol. 97, no. 4, pp. 2189-2199, 2019.
[21]R. Datta, R. Dey, B. Bhattacharya, R. Saravanakumar, and C. K. Ahn, New double integral inequality with application to stability analysis for linear retarded systems, IET Control Theory & Applications, vol. 13, no. 10, pp. 1514-1524, 2019.
[22]N. Gunasekaran, R. Saravanakumar, Y. H. Joo, and H. S. Kim, Finite-time synchronization of sampled-data T-S fuzzy complex dynamical networks subject to average dwell-time approach, Fuzzy Sets and Systems, Vol. 374, pp. 40-59, 2019.
[23]S. Saravanan, M. S. Ali, and R. Saravanakumar, Finite-time non-fragile dissipative stabilization of delayed neural networks, Neural Processing Letters, Vol. 49, no. 2, pp. 573-591, 2019.
[24]R. Saravanakumar, G. Rajchakit, M. S. Ali, and Y. H. Joo, Exponential dissipativity criteria for generalized BAM neural networks with variable delays, Neural Computing and Applications, vol. 31, no. 7, pp. 2717-2726, 2019.
[25]M. S. Ali, R. Vadivel, and R. Saravanakumar, Event-triggered state estimation for Markovian jumping impulsive neural networks with interval time-varying delays, International Journal of Control, vol. 92, no. 2, pp. 270-290, 2019.
[26]R. Saravanakumar, S. B. Stojanovic, D. D. Radosavljevic, C. K. Ahn, and H. R. Karimi, Finite-time passivity-based stability criteria for delayed discrete-time neural networks via new weighted summation inequalities, IEEE Transactions on neural networks and learning systems, Vol. 30, no. 1, pp. 58-71, 2018.
[27]R. Saravanakumar, H. S. Kang, C. K. Ahn, X. Su, and H. R. Karimi, Robust stabilization of delayed neural networks: Dissipativity-learning approach,IEEE Transactions on neural networks and learning systems, Vol. 30, no. 3, pp. 913-922, 2018.
[28]M. S. Ali, R. Vadivel, and R. Saravanakumar, Design of robust reliable control for T-S fuzzy Markovian jumping delayed neutral type neural networks with probabilistic actuator faults and leakage delays: An event-triggered communication scheme, ISA Transactions, Vol. 77, pp. 30-48, 2018.
[29]R. Saravanakumar, M. S. Ali, H. Huang, J. Cao, and Y. H. Joo, Robust H∞ state-feedback control for nonlinear uncertain systems with mixed time-varying delays, International Journal of Control, Automation and Systems, Vol. 16, no. 1, pp. 225-233, 2018.
[30]M. S. Ali, N. Gunasekaran, and R. Saravanakumar, Design of passivity and passification for delayed neural networks with Markovian jump parameters via non-uniform sampled-data control, Neural Computing and Applications, vol. 30, no. 2, pp. 595-605, 2018.
[31]G. Rajchakit and R. Saravanakumar, Exponential stability of semi-Markovian jump generalized neural networks with interval time-varying delays, Neural Computing and Applications, Vol. 29, no. 2, pp. 483-492, 2018.
[32]T. Radhika, G. Nagamani, Q. Zhu, S. Ramasamy, and R. Saravanakumar, Further results on dissipativity analysis for Markovian jump neural networks with randomly occurring uncertainties and leakage delays, Neural Computing and Applications, Vol. 30, no. 11, pp. 3565-3579, 2018.
[33]R. Saravanakumar, G. Rajchakit, M. S. Ali, Z. Xiang, and Y. H. Joo, Robust extended dissipativity criteria for discrete-time uncertain neural networks with time-varying delays, Neural Computing and Applications, Vol. 30, no. 12, pp. 3893-3904, 2018.
[34]R. Saravanakumar, G. Rajchakit, C. K. Ahn, and H. R. Karimi, Exponential stability, passivity, and dissipativity analysis of generalized neural networks with mixed time-varying delays, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 49, no. 2, pp. 395-405, 2017.
[35]R. Saravanakumar, G. Rajchakit, M. S. Ali, and Y. H. Joo, Extended dissipativity of generalised neural networks including time delays, International Journal of Systems Science, vol. 48, no. 11, pp. 2311-2320, 2017.
[36]G. Rajchakit, R. Saravanakumar, C. K. Ahn, and H. R. Karimi, Improved exponential convergence result for generalized neural networks including interval time-varying delayed signals, Neural Networks, vol. 86, pp. 10-17, 2017.
[37]M. S. Ali, R. Saravanakumar, C. K. Ahn, and H. R. Karimi, Stochastic H∞ filtering for neural networks with leakage delay and mixed time-varying delays, Information Sciences, vol. 388, pp. 118-134, 2017.
[38]R. Saravanakumar, M. S. Ali, and H. R. Karimi, Robust H∞ control of uncertain stochastic Markovian jump systems with mixed time-varying delays, International Journal of Systems Science, vol. 48, no. 4, pp. 862-872, 2017.
[39]R. Saravanakumar and M. S. Ali, Robust H∞ control for uncertain Markovian jump systems with mixed delays, Chinese Physics B, vol. 25, no. 7, p. 070201, 2016.
[40]R. Saravanakumar, M. S. Ali, C. K. Ahn, H. R. Karimi, and P. Shi, Stability of Markovian jump generalized neural networks with interval time-varying delays, IEEE Transactions on neural networks and learning systems, vol. 28, no. 8, pp. 1840-1850, 2016.
[41]M. Syed Ali and R. Saravanakumar, Improved H∞ performance analysis of uncertain Markovian jump systems with overlapping time-varying delays, Complexity, vol. 21, no. S1, pp. 460-477, 2016
[42]R. Saravanakumar, M. S. Ali, J. Cao, and H. Huang, H∞ state estimation of generalised neural networks with interval time-varying delays, International Journal of Systems Science, vol. 47, no. 16, pp. 3888-3899, 2016.
[43]R. Saravanakumar, M. S. Ali, and M. Hua, H∞ state estimation of stochastic neural networks with mixed time-varying delays, Soft Computing, vol. 20, no. 9, pp. 3475-3487, 2016.
[44]M. S. Ali, R. Saravanakumar, and J. Cao, New passivity criteria for memristor-based neutral-type stochastic BAM neural networks with mixed time-varying delays, Neurocomputing, vol. 171, pp. 1533-1547, 2016.
[45]M. S. Ali, R. Saravanakumar, and S. Arik, Novel H∞ state estimation of static neural networks with interval time-varying delays via augmented Lyapunov-Krasovskii functional, Neurocomputing, vol. 171, pp. 949-954, 2016.
[46]M. S. Ali and R. Saravanakumar, Robust H∞ control of uncertain systems with two additive time-varying delays, Chinese Physics B, vol. 24, no. 9, p. 090202, 2015.
[47]M. S. Ali, R. Saravanakumar, and Q. Zhu, Less conservative delay-dependent H∞ control of uncertain neural networks with discrete interval and distributed time-varying delays, Neurocomputing, vol. 166, pp. 84-95, 2015.
[48]M. S. Ali, S. Arik, and R. Saravanakumar, Delay-dependent stability criteria of uncertain Markovian jump neural networks with discrete interval and distributed time-varying delays, Neurocomputing, vol. 158, pp. 167-173, 2015.
[49]M. S. Ali and R. Saravanakumar, Augmented Lyapunov approach to H∞ state estimation of static neural networks with discrete and distributed time-varying delays, Chinese Physics B, vol. 24, no. 5, p. 050201, 2015.
[50]M. S. Ali and R. Saravanakumar, Novel delay-dependent robust H∞ control of uncertain systems with distributed time-varying delays, Applied Mathematics and Computation, vol. 249, pp. 510-520, 2014.
[51]M. S. Ali and R. Saravanakumar, Improved delay-dependent robust H∞ control of an uncertain stochastic system with interval time-varying and distributed delays, Chinese Physics B, vol. 23, no. 12, p. 120201, 2014.
International conference publications:
[1]R. Saravanakumar and M. S. Ali, "H∞ state estimation control of neural networks with distributed time-varying delays," in 2014 International Conference on Soft Computing and Machine Intelligence, pp. 11-14, IEEE, 2014.
[2]R. Saravanakumar and M. S. Ali, "Delay-dependent stability criteria of neural networks with interval and distributed time-varying delays," in 2014 International Conference on Mathematical Sciences, pp. 613-617, Elsevier, 2014.
[3]R. Saravanakumar, M. S. Ali, and G. Rajchakit, "Improved stability analysis of delayed neural networks via Wirtinger-based double integral inequality," in 2016 International Conference on Inventive Computation Technologies (ICICT), Vol. 3, pp. 1-5, IEEE, 2016.
[4]H. Mukaidani, R. Saravanakumar, and H. Xu, "Open-loop dynamic games for interconnected positive nonlinear systems with H∞ constraint," in 2019 12th Asian Control Conference (ASCC), pp. 515-520, IEEE, 2019.
[5]R. Saravanakumar, A. Kazemy, and H. Mukaidani, "Robust dissipative sampled-data control of offshore steel jacket platforms," in 2019 Society of Instrument and Control Engineers (SICE), IEEE, 2019, DOI: 10.23919/SICE.2019.8859804
[6]M. Sagara, H. Mukaidani, R. Saravanakumar, and H. Xu, "Gain-scheduled robust Pareto static output feedback strategy for stochastic LPV systems," in 2019 Society of Instrument and Control Engineers (SICE 2019), IEEE, 2019, DOI: 10.23919/SICE.2019.8859954
[7]H. Mukaidani, R. Saravanakumar, H. Xu, and M. Sagara, "Robust Nash strategy for uncertain delay systems with LSTM and its application for TCP/AQM congestion control," in 2019 Society of Instrument and Control Engineers (SICE 2019), IEEE, 2019, DOI: 10.23919/SICE.2019.8859817
[8]H. Mukaidani, R. Saravanakumar, H. Xu, and W. Zhuang, "Robust Nash static output feedback strategy for uncertain Markov jump delay stochastic systems," in 2019 IEEE 58th Conference on Decision and Control (CDC), DOI: 10.1109/CDC40024.2019.9028961
[9]R. Saravanakumar, H. Mukaidani, and Amir Amini, “Non-fragile Exponential Consensus of Nonlinear Multi-agent Systems via Sampled-data Control,” IFAC-PapersOnLine, Vol.53, no.2, pp. 5677-5682, 2020
[10]H. Mukaidani, R. Saravanakumar, H. Xu, and W. Zhuang, “Robust Incentive Stackelberg Strategy for Markov Jump Delay Stochastic Systems via Static Output Feedback,” IFAC-PapersOnLine, Vol.53, no.2, pp. 6709-6714, 2020
[11]H. Mukaidani, R. Saravanakumar, H. Xu, and W. Zhuang, “Robust Stackelberg Games via Static Output Feedback Strategy for Uncertain Stochastic Systems with State Delay,” IFAC-PapersOnLine, Vol.53, no.2, pp. 7154-7159, 2020
[12]R. Saravanakumar, H. Mukaidani, “Dissipativity-Based State Estimation for Uncertain Fuzzy Stochastic Neural Networks” IEEE International Conference on Systems, Man, and Cybernetics, pp. 2830-2835, 2020
[13]Zhouning Du, H. Mukaidani, R. Saravanakumar, “Action recognition based on linear dynamical systems with deep features in videos” IEEE International Conference on Systems, Man, and Cybernetics, pp. 2634-2639, 2020
[14]H Kikuchi, H Mukaidani, R. Saravanakumar, W Zhuang, “Robust Vaccination Strategy based on Dynamic Game for Uncertain SIR Time-Delay Model” IEEE International Conference on Systems, Man, and Cybernetics, pp. 3427-3432, 2020
[15]R. Saravanakumar, “Further Stability and L2-Gain Conditions for Sampled-Data Systems”, International conference on Soft Computing and Machine Intelligence 2021, IEEE xplore, to appear.
[16]Rupak Datta, R. Saravanakumar, “Dissipative Control for Delayed T–S fuzzy System with Data Packet Dropout”, International conference on Soft Computing and Machine Intelligence 2021, IEEE xplore, to appear.
Talks：
·2021.09 Lakshmi College of Education, Tamilnadu, India;
·2020.02 International Conference on Mathematical Modeling and Scientific Computing, Gandhigram Rural Institute-DU, India;
·2019.07 Department of Mathematics, Gandhigram Rural Institute-DU, India;
·2016.07 Faculty of Science, Rajabhat University, Phuket, Thailand.
Active Reviewer in SCI(E) Journals
Mathematics Reviews; Applied Mathematics and Computation; Neurocomputing; IEEE Systems Journal; IEEE Transactions on Neural Networks and Learning Systems; IEEE Transactions on Cybernetics; IEEE Transactions on Fuzzy Systems; IEEE Transactions on Systems, Man and Cybernetics: Systems; IEEE Access; IEEE Transactions on Network Science and Engineering; IEEE Transactions on Emerging Topics in Computaional Intelligence; IEEE Transactions on Circuits and Systems II: Express Briefs; IEEE Transactions on Circuits and Systems I: Regular Papers; IEEE Transactions on Aerospace and Electronic Systems; Journal of the Franklin Institute; International Journal of Systems Science; International Journal of Control, Automation and Systems; Information Sciences; Nonlinear Dynamics; International Journal of Robust and Nonlinear Control; International Journal of Fuzzy Systems; Neural Processing Letters; ISA Transactions; Communications in Nonlinear Science and Numerical Simulation; Chaos, solitons & fractals; Advances in Differential Equations; Pramana – Journal of Physics; Cognitive Neurodynamics; Intelligent automation & Soft Computing; International Journal of Electronics; Complexity; Journal of Physics; Fluctuation and Noise Letters; Soft Computing and Automation Journal; Journal of Electrical Engineering & Technology; Etc.
