Abstract
In this study, we propose an advanced methodology for analyzing the exponential synchronization of semi-Markovian jump neural networks (SMJNNs) subjected to time-varying delay and controlled by a sampled-data controller. The analysis is based on a Wirtinger-based integral inequality (Li in Nonlinear Analysis Hybrid Systems 41:101028, 2021) and modified free matrix-based integral inequality (MFMBII) (Zeng in SN Applied Sciences 5:301, 2023), which provide a powerful framework for investigating complex dynamical systems. First, we establish a MFMBII, incorporating the dynamics of the SMJNNs and the time-varying delay. This inequality allows us to derive sufficient conditions for the exponential synchronization of the network systems. Then we proceed to derive two sufficient conditions that pertain to the design of the sampled-data controller. These conditions ensure the mean square input-to-state stability (ISS) for the hybrid closed-loop system. To achieve this, we employ the Lyapunov–Krasovskii functional (LKF) and the MFMBII approach. Lastly, the proposed input-to-state stabilization method is demonstrated numerically by using a numerical example that is used to verify its validity.
Similar content being viewed by others
Data Availability Statement
Data sharing not applicable to this article as no data-sets were generated or analyzed during the current study.
References
K. Udhayakumar, S. Shanmugasundaram, A. Kashkynbayev, K. Janani, R. Rakkiyappan, Saturated and asymmetric saturated impulsive control synchronization of coupled delayed inertial neural networks with time-varying delays. Appl. Math. Modell. 113, 528–544 (2023)
Y. Cui, P. Cheng, Exponential synchronization of stochastic time-delayed memristor-based neural networks via pinning impulsive control. Int. J. Control Autom. Syst. 22, 1–10 (2024)
Y. Sheng, H. Gong, Z. Zeng, Global synchronization of complex-valued neural networks with unbounded time-varying delays. Neural Netw. 162, 309–317 (2023)
B. Adhira, G. Nagamani, D. Dafik, Non-fragile extended dissipative synchronization control of delayed uncertain discrete-time neural networks. Commun. Nonlinear Sci. Numer. Simul. 116, 106820 (2023)
C. Ge, C. Chang, Y. Liu, C. Hua, Dynamic event-triggered exponential synchronization for neural networks with random controller gain perturbations. Int. J. Control Autom. Syst. 21, 2927–2937 (2023)
J. Fang, N. Yin, D. Wei, H. Liu, W. Deng, Improved finite-time synchronization of coupled discontinuous neural networks under adaptive sliding mode control. Int. J. Dyn. Control 12, 396–408 (2024)
A. Kumar, S. Das, V.K. Rajeev Yadav, Global exponential synchronization of complex-valued recurrent neural networks in presence of uncertainty along with time-varying bounded and unbounded delay terms. Int. J. Dyn. Control 10, 902–916 (2022)
X. Wang, Y. Ma, Adaptive non-fragile sliding mode control for switched semi-Markov jump system with time-delay and attack via reduced-order method. Appl. Math. Comput. 440, 127670 (2023)
Z. Zhang, H. Shen, L. Su, \( H_{\infty }\)/Passive synchronization of semi-Markov jump neural networks subject to hybrid attacks via an activation function division approach. J. Syst. Sci. Complex. 37, 1023–1036 (2024)
L. Han, Y. Ma, Learning-based asynchronous sliding mode control for semi-Markov jump systems with time-varying delay using relaxed negative-determination lemma. Chaos Solitons Fractals 179, 114454 (2024)
N. Aravinth, R. Sakthivel, A. Mohammadzadeh, S. Saat, Stabilization of delayed semi-Markov jump neural networks with actuator faults: a quantized hybrid control approach. Nonlinear Anal. Hybrid Syst. 54, 101509 (2024)
D. Cao, Y. Jin, W. Qi, Synchronization for stochastic semi-Markov jump neural networks with dynamic event-triggered scheme. J. Franklin Inst. 360, 12620–12639 (2023)
C. Ge, C. Chang, Y. Liu, C. Liu, Sampled-data-based exponential synchronization of switched coupled neural networks with unbounded delay. Commun. Nonlinear Sci. Numer. Simul. 117, 106931 (2023)
X.Z. Pan, J.J. Huang, S.M. Lee, A novel convex relaxation technique on affine transformed sampled-data control issue for fuzzy semi-Markov jump systems. Appl. Math. Comput. 451, 128026 (2023)
T. Yang, Z. Wang, J. Xia, H. Shen, Sampled-data exponential synchronization of stochastic chaotic Lur’e delayed systems. Math. Comput. Simul. 203, 44–57 (2023)
M. Huan, C. Li, Synchronization of reaction diffusion neural networks with sampled-data control via a new two-sided looped-functional. Chaos Solitons Fractals 167, 113059 (2023)
H. Wang, Y. Ni, J. Wang, J. Tian, C. Ge, Sampled-data control for synchronization of Markovian jumping neural networks with packet dropout. Appl. Intell. 53, 8898–8909 (2023)
C.K. Ahn, Input-to-state stable nonlinear filtering for a class of continuous-time delayed nonlinear systems. Int. J. Control 86, 1179–1185 (2013)
Z. Zhang, Z. Yan, J. Zhou, Y. Chen, Adaptive input-to-state stable synchronization for uncertain time-delay Lur’e systems. Commun. Theor. Phys. 73, 1–10 (2021)
L. He, W. Wu, G. Yao, J. Zhou, Input-to-state stabilization of delayed semi-Markovian jump neural networks via sampled-data control. Neural Process. Lett. 55, 3245–3266 (2023)
L. He, W. Wu, J. Zhou, G. Yao, Input-to-state stable synchronization for delayed Lurie systems via sampled-data control. Discrete Contin. Dyn. Syst. B 28, 1553–1570 (2023)
X. Li, S.K. Nguang, K. She, J. Cheng, K. Shi, S. Zhong, Stochastic exponential synchronization for delayed neural networks with semi-Markovian switchings: saturated heterogeneous sampling communication. Nonlinear Anal. Hybrid Syst. 41, 101028 (2021)
Y. Zhang, Y. He, F. Long, Augmented two-side-looped Lyapunov functional for sampled-data-based synchronization of chaotic neural networks with actuator saturation. Neurocomputing 422, 287–294 (2021)
Q. Zeng, M. Jiang, J. Hu, Free-matrix-based integral inequalities for sampled-data synchronization control of delayed complex networks. SN Appl. Sci. 5, 301 (2023)
N. Gunasekaran, G. Zhai, Q. Yu, Sampled-data synchronization of delayed multi-agent networks and its application to coupled circuit. Neurocomputing 413, 499–511 (2020)
J. Zhou, Y. Liu, J. Xia, Z. Wang, S. Arik, Resilient fault-tolerant anti-synchronization for stochastic delayed reaction-diffusion neural networks with semi-Markov jump parameters. Neural Netw. 125, 194–204 (2020)
L. Zhang, S.K. Nguang, D. Ouyang, S. Yan, Synchronization of delayed neural networks via integral-based event-triggered scheme. IEEE Trans. Neural Netw. Learn. Syst. 31, 5092–5102 (2020)
M. Prakash, P. Balasubramaniam, S. Lakshmanan, Synchronization of Markovian jumping inertial neural networks and its applications in image encryption. Neural Netw. 83, 86–93 (2016)
Funding
There are no funders to report for this submission.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
This work does not have any Conflict of interest.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Kumar, S.S., Chandrasekar, A. Sampled-data control with actuator saturated exponential synchronization semi-Markovian jump neural networks subject to input-to-state stability approach. Eur. Phys. J. Plus 139, 683 (2024). https://doi.org/10.1140/epjp/s13360-024-05470-y
Received:
Accepted:
Published:
DOI: https://doi.org/10.1140/epjp/s13360-024-05470-y