Unified synchronization and fault‐tolerant anti‐disturbance control for synchronization of multiple memristor‐based neural networks
Summary This work primarily concentrates on the design of fault‐tolerant anti‐disturbance control for synchronization of multiple memristor‐based neural networks subject to time delay, matched and mismatched disturbances. Moreover, in the addressed network model, we consider parameter uncertainties...
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Published in | International journal of robust and nonlinear control Vol. 34; no. 4; pp. 2849 - 2864 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Bognor Regis
Wiley Subscription Services, Inc
10.03.2024
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Subjects | |
Online Access | Get full text |
ISSN | 1049-8923 1099-1239 |
DOI | 10.1002/rnc.7112 |
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Abstract | Summary
This work primarily concentrates on the design of fault‐tolerant anti‐disturbance control for synchronization of multiple memristor‐based neural networks subject to time delay, matched and mismatched disturbances. Moreover, in the addressed network model, we consider parameter uncertainties and actuator faults. Firstly in order to estimate the matched disturbances generated by the exogenous system, a disturbance observer is devised. Whereas, the mismatched part is tackled by employing the mixed ℋ∞$$ {\mathscr{H}}_{\infty } $$ and passivity performance indexes. Subsequently, a unified controller is designed by incorporating error feedback control and the disturbance estimate. Further, with the assistance of Lyapunov stability theory and linear matrix inequality technique, an adequate criteria is procured to ascertain the required synchronization criteria for the assayed network model with the mixed ℋ∞$$ {\mathscr{H}}_{\infty } $$ and passivity performance indexes. Following this, by basing on the established conditions, the explicit form of the controller and observer gain matrices is obtained. In the end, a numerical example with simulation results is shown to confirm the potential and usefulness of the conclusions acquired from the theoretical analysis. |
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AbstractList | Summary
This work primarily concentrates on the design of fault‐tolerant anti‐disturbance control for synchronization of multiple memristor‐based neural networks subject to time delay, matched and mismatched disturbances. Moreover, in the addressed network model, we consider parameter uncertainties and actuator faults. Firstly in order to estimate the matched disturbances generated by the exogenous system, a disturbance observer is devised. Whereas, the mismatched part is tackled by employing the mixed ℋ∞$$ {\mathscr{H}}_{\infty } $$ and passivity performance indexes. Subsequently, a unified controller is designed by incorporating error feedback control and the disturbance estimate. Further, with the assistance of Lyapunov stability theory and linear matrix inequality technique, an adequate criteria is procured to ascertain the required synchronization criteria for the assayed network model with the mixed ℋ∞$$ {\mathscr{H}}_{\infty } $$ and passivity performance indexes. Following this, by basing on the established conditions, the explicit form of the controller and observer gain matrices is obtained. In the end, a numerical example with simulation results is shown to confirm the potential and usefulness of the conclusions acquired from the theoretical analysis. This work primarily concentrates on the design of fault‐tolerant anti‐disturbance control for synchronization of multiple memristor‐based neural networks subject to time delay, matched and mismatched disturbances. Moreover, in the addressed network model, we consider parameter uncertainties and actuator faults. Firstly in order to estimate the matched disturbances generated by the exogenous system, a disturbance observer is devised. Whereas, the mismatched part is tackled by employing the mixed ℋ∞$$ {\mathscr{H}}_{\infty } $$ and passivity performance indexes. Subsequently, a unified controller is designed by incorporating error feedback control and the disturbance estimate. Further, with the assistance of Lyapunov stability theory and linear matrix inequality technique, an adequate criteria is procured to ascertain the required synchronization criteria for the assayed network model with the mixed ℋ∞$$ {\mathscr{H}}_{\infty } $$ and passivity performance indexes. Following this, by basing on the established conditions, the explicit form of the controller and observer gain matrices is obtained. In the end, a numerical example with simulation results is shown to confirm the potential and usefulness of the conclusions acquired from the theoretical analysis. This work primarily concentrates on the design of fault‐tolerant anti‐disturbance control for synchronization of multiple memristor‐based neural networks subject to time delay, matched and mismatched disturbances. Moreover, in the addressed network model, we consider parameter uncertainties and actuator faults. Firstly in order to estimate the matched disturbances generated by the exogenous system, a disturbance observer is devised. Whereas, the mismatched part is tackled by employing the mixed and passivity performance indexes. Subsequently, a unified controller is designed by incorporating error feedback control and the disturbance estimate. Further, with the assistance of Lyapunov stability theory and linear matrix inequality technique, an adequate criteria is procured to ascertain the required synchronization criteria for the assayed network model with the mixed and passivity performance indexes. Following this, by basing on the established conditions, the explicit form of the controller and observer gain matrices is obtained. In the end, a numerical example with simulation results is shown to confirm the potential and usefulness of the conclusions acquired from the theoretical analysis. |
Author | Satheesh, T. Aravinth, N. Sakthivel, R. Karimi, H.R. |
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Cites_doi | 10.1109/TCYB.2019.2945580 10.1109/JAS.2022.105566 10.1109/TCT.1971.1083337 10.1016/j.amc.2021.126839 10.1016/j.jfranklin.2021.03.026 10.1016/j.jfranklin.2020.09.016 10.1016/j.knosys.2021.107395 10.1109/TNNLS.2020.2985860 10.1109/TCYB.2020.2983481 10.1016/j.chaos.2022.113034 10.1016/j.neunet.2021.06.019 10.1016/j.jfranklin.2018.05.016 10.1016/j.physa.2022.127107 10.1016/j.isatra.2018.09.006 10.1109/TSMC.2021.3101202 10.1109/TCYB.2019.2953236 10.1109/JAS.2022.105692 10.1109/TSMC.2022.3201671 10.1109/TCSI.2020.3004170 10.1002/acs.3283 10.1016/j.knosys.2022.109338 10.1109/TAC.2018.2819683 10.1016/j.ins.2021.09.027 10.1109/TFUZZ.2020.2998519 10.1016/j.nahs.2018.11.001 10.1109/TNNLS.2018.2874035 10.1109/TIE.2019.2902825 10.1016/j.physa.2021.126431 10.1016/j.ins.2020.08.063 10.1016/j.jfranklin.2019.09.015 10.1016/j.jfranklin.2020.03.031 10.1109/TSMC.2021.3061768 10.1109/TNNLS.2020.3009081 10.1002/rnc.6036 10.1038/nature06932 10.1109/TSMC.2021.3071811 10.1016/j.knosys.2022.109104 10.1109/ACCESS.2021.3089374 10.1109/TASE.2022.3184022 10.1109/TNNLS.2020.3017171 |
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References | 2021; 9 2022; 596 2022; 252 2022; 250 2019; 51 2019; 50 2021; 544 2019; 32 2023; 167 2018; 63 2018; 83 2022; 418 2020; 32 2021; 143 2021; 52 2022; 415 2018; 47 2023; 20 2021; 35 2022; 585 2022; 582 2020; 51 2019; 66 1971; 18 2018; 355 2021; 358 2022; 9 2020; 357 2019; 356 2021; 230 2018; 30 2022; 52 2022; 53 2020; 67 2022; 32 2008; 453 2020; 29 e_1_2_9_30_1 e_1_2_9_31_1 e_1_2_9_11_1 e_1_2_9_34_1 e_1_2_9_10_1 e_1_2_9_35_1 e_1_2_9_13_1 e_1_2_9_32_1 e_1_2_9_12_1 e_1_2_9_33_1 e_1_2_9_15_1 e_1_2_9_38_1 e_1_2_9_14_1 e_1_2_9_39_1 e_1_2_9_17_1 e_1_2_9_36_1 e_1_2_9_16_1 e_1_2_9_37_1 e_1_2_9_19_1 e_1_2_9_18_1 e_1_2_9_41_1 e_1_2_9_20_1 e_1_2_9_40_1 e_1_2_9_22_1 e_1_2_9_21_1 e_1_2_9_24_1 e_1_2_9_43_1 e_1_2_9_23_1 e_1_2_9_8_1 Song S (e_1_2_9_42_1) 2018; 47 e_1_2_9_7_1 e_1_2_9_6_1 e_1_2_9_4_1 e_1_2_9_3_1 e_1_2_9_2_1 Zhu S (e_1_2_9_5_1) 2022; 415 e_1_2_9_9_1 e_1_2_9_26_1 e_1_2_9_25_1 e_1_2_9_28_1 e_1_2_9_27_1 e_1_2_9_29_1 |
References_xml | – volume: 418 year: 2022 article-title: Disturbance rejection using SMC‐based‐equivalent‐input‐disturbance approach publication-title: Appl Math Comput – volume: 9 start-page: 1262 year: 2022 end-page: 1272 article-title: Discounted iterative adaptive critic designs with novel stability analysis for tracking control publication-title: IEEE/CAA J Automat Sin – volume: 47 start-page: 443 year: 2018 end-page: 462 article-title: Mixed /passive projective synchronization for nonidentical uncertain fractional‐order neural networks based on adaptive sliding mode control publication-title: Neural Process Lett – volume: 83 start-page: 13 year: 2018 end-page: 24 article-title: Robust control for networked control systems with randomly occurring uncertainties: observer‐based case publication-title: ISA Trans – volume: 415 year: 2022 article-title: Event‐triggered synchronization of coupled memristive neural networks publication-title: Appl Math Comput – volume: 20 start-page: 1663 year: 2023 end-page: 1674 article-title: Adaptive neural self‐triggered bipartite fault‐tolerant control for nonlinear MASs with dead‐zone constraints publication-title: IEEE Trans Autom Sci Eng – volume: 53 start-page: 1584 year: 2022 end-page: 1595 article-title: Dual event‐triggered constrained control through adaptive critic for discrete‐time zero‐sum games publication-title: IEEE Trans Syst Man Cybern: Syst – volume: 9 start-page: 89461 year: 2021 end-page: 89475 article-title: Finite‐time mixed /passivity for neural networks with mixed interval time‐varying delays using the multiple integral Lyapunov‐Krasovskii functional publication-title: IEEE Access – volume: 167 year: 2023 article-title: Reinforcement learning‐based decentralized fault tolerant control for constrained interconnected nonlinear systems publication-title: Chaos Solitons Fract – volume: 9 start-page: 893 year: 2022 end-page: 906 article-title: Active disturbance rejection control for uncertain nonlinear systems with sporadic measurements publication-title: IEEE/CAA J Automat Sin – volume: 596 year: 2022 article-title: Finite‐time adaptive synchronization of coupled uncertain neural networks via intermittent control publication-title: Phys A: Stat Mech Appl – volume: 250 year: 2022 article-title: Fixed‐time synchronization for inertial Cohen‐Grossberg delayed neural networks: an event‐triggered approach publication-title: Knowl‐Based Syst – volume: 230 year: 2021 article-title: Finite‐time stabilization of memristor‐based inertial neural networks with time‐varying delays combined with interval matrix method publication-title: Knowl‐Based Syst – volume: 357 start-page: 6352 year: 2020 end-page: 6369 article-title: Non‐fragile mixed passive and state estimation for singularly perturbed neural networks with semi‐Markov jumping parameters publication-title: J Franklin Inst – volume: 355 start-page: 4897 year: 2018 end-page: 4915 article-title: Composite fault‐tolerant control with disturbance observer for stochastic systems with multiple disturbances publication-title: J Franklin Inst – volume: 18 start-page: 507 year: 1971 end-page: 519 article-title: Memristor‐the missing circuit element publication-title: IEEE Trans Circuit Theory – volume: 453 start-page: 80 year: 2008 end-page: 83 article-title: The missing memristor found publication-title: Nature – volume: 32 start-page: 2952 year: 2020 end-page: 2964 article-title: Gain‐scheduled finite‐time synchronization for reaction‐diffusion memristive neural networks subject to inconsistent Markov chains publication-title: IEEE Trans Neural Netw Learn Syst – volume: 52 start-page: 2994 year: 2021 end-page: 3004 article-title: ℌ state estimation for switched inertial neural networks with time‐varying delays: a persistent dwell‐time scheme publication-title: IEEE Trans Syst Man Cybern: Syst – volume: 252 year: 2022 article-title: Finite‐time dissipative control for bidirectional associative memory neural networks with state‐dependent switching and time‐varying delays publication-title: Knowl‐Based Syst – volume: 51 start-page: 427 year: 2020 end-page: 437 article-title: Periodic event‐triggered synchronization of multiple memristive neural networks with switching topologies and parameter mismatch publication-title: IEEE Trans Cybern – volume: 32 start-page: 4281 year: 2022 end-page: 4299 article-title: Disturbance estimation and synchronization control design for nonlinear complex dynamical networks with input delays publication-title: Int J Robust Nonlinear Control – volume: 51 start-page: 2944 year: 2019 end-page: 2955 article-title: Finite‐time and fixed‐time synchronization of coupled memristive neural networks with time delay publication-title: IEEE Trans Cybern – volume: 544 start-page: 395 year: 2021 end-page: 414 article-title: Fault tolerant sampled‐data control for networked control systems with probabilistic time‐varying delay publication-title: Inform Sci – volume: 67 start-page: 5233 year: 2020 end-page: 5245 article-title: Robust pinning synchronization for complex networks with event‐triggered communication scheme publication-title: IEEE Trans Circuits Syst I: Regul Pap – volume: 356 start-page: 9928 year: 2019 end-page: 9952 article-title: Finite/fixed‐time synchronization control of coupled memristive neural networks publication-title: J Franklin Inst – volume: 66 start-page: 8947 year: 2019 end-page: 8957 article-title: Uncertainty and disturbance estimator based controller equipped with a multiple‐time‐delayed filter to improve the voltage quality of inverters publication-title: IEEE Trans Ind Electron – volume: 29 start-page: 2349 year: 2020 end-page: 2360 article-title: Sliding‐mode control of fuzzy singularly perturbed descriptor systems publication-title: IEEE Trans Fuzzy Syst – volume: 52 start-page: 3568 year: 2021 end-page: 3580 article-title: Sampled‐data‐based event‐triggered synchronization strategy for fractional and impulsive complex networks with switching topologies and time‐varying delay publication-title: IEEE Trans Syst Man Cybern: Syst – volume: 30 start-page: 1841 year: 2018 end-page: 1853 article-title: Nonfragile dissipative synchronization for Markovian memristive neural networks: a gain‐scheduled control scheme publication-title: IEEE Trans Neural Netw Learn Syst – volume: 50 start-page: 4008 year: 2019 end-page: 4019 article-title: Coordination control for uncertain networked systems using interval observers publication-title: IEEE Trans Cybern – volume: 357 start-page: 12308 year: 2020 end-page: 12325 article-title: Fixed‐time synchronization of delayed complex dynamical systems with stochastic perturbation via impulsive pinning control publication-title: J Franklin Inst – volume: 32 start-page: 65 year: 2019 end-page: 78 article-title: Robust non‐fragile fault detection filter design for delayed singular Markovian jump systems with linear fractional parametric uncertainties publication-title: Nonlinear Anal: Hybrid Syst – volume: 143 start-page: 377 year: 2021 end-page: 385 article-title: Neural adaptive fault‐tolerant finite‐time control for nonstrict feedback systems: an event‐triggered mechanism publication-title: Neural Netw – volume: 63 start-page: 4345 year: 2018 end-page: 4352 article-title: Composite robust control for uncertain stochastic nonlinear systems with state delay via a disturbance observer publication-title: IEEE Trans Automat Contr – volume: 585 year: 2022 article-title: Finite‐time bipartite synchronization of coupled neural networks with uncertain parameters publication-title: Phys A: Stat Mech Appl – volume: 32 start-page: 1642 year: 2020 end-page: 1653 article-title: Bipartite synchronization of multiple memristor‐based neural networks with antagonistic interactions publication-title: IEEE Trans Neural Netw Learn Syst – volume: 358 start-page: 4602 year: 2021 end-page: 4625 article-title: Observer‐based synchronization of fractional‐order Markovian jump multi‐weighted complex dynamical networks subject to actuator faults publication-title: J Franklin Inst – volume: 32 start-page: 4191 year: 2020 end-page: 4201 article-title: Synchronization of switched discrete‐time neural networks via quantized output control with actuator fault publication-title: IEEE Trans Neural Netw Learn Syst – volume: 52 start-page: 4611 year: 2022 end-page: 4622 article-title: Robust stabilization for a class of nonlinear positive systems with multiple disturbances publication-title: IEEE Trans Syst Man Cybern: Syst – volume: 35 start-page: 1664 year: 2021 end-page: 1684 article-title: Small‐gain technique‐based adaptive fuzzy command filtered control for uncertain nonlinear systems with unmodeled dynamics and disturbances publication-title: Int J Adapt Control Signal Process – volume: 582 start-page: 302 year: 2022 end-page: 315 article-title: Stability of stochastic delay switched neural networks with all unstable subsystems: a multiple discretized Lyapunov‐Krasovskii functionals method publication-title: Inform Sci – volume: 47 start-page: 443 year: 2018 ident: e_1_2_9_42_1 article-title: Mixed H∞$$ {\mathcal{H}}_{\infty } $$/passive projective synchronization for nonidentical uncertain fractional‐order neural networks based on adaptive sliding mode control publication-title: Neural Process Lett – ident: e_1_2_9_18_1 doi: 10.1109/TCYB.2019.2945580 – ident: e_1_2_9_33_1 doi: 10.1109/JAS.2022.105566 – ident: e_1_2_9_3_1 doi: 10.1109/TCT.1971.1083337 – ident: e_1_2_9_31_1 doi: 10.1016/j.amc.2021.126839 – ident: e_1_2_9_28_1 doi: 10.1016/j.jfranklin.2021.03.026 – ident: e_1_2_9_14_1 doi: 10.1016/j.jfranklin.2020.09.016 – ident: e_1_2_9_10_1 doi: 10.1016/j.knosys.2021.107395 – ident: e_1_2_9_8_1 doi: 10.1109/TNNLS.2020.2985860 – ident: e_1_2_9_9_1 doi: 10.1109/TCYB.2020.2983481 – ident: e_1_2_9_26_1 doi: 10.1016/j.chaos.2022.113034 – ident: e_1_2_9_24_1 doi: 10.1016/j.neunet.2021.06.019 – ident: e_1_2_9_39_1 doi: 10.1016/j.jfranklin.2018.05.016 – ident: e_1_2_9_21_1 doi: 10.1016/j.physa.2022.127107 – ident: e_1_2_9_23_1 doi: 10.1016/j.isatra.2018.09.006 – ident: e_1_2_9_37_1 doi: 10.1109/TSMC.2021.3101202 – ident: e_1_2_9_7_1 doi: 10.1109/TCYB.2019.2953236 – ident: e_1_2_9_13_1 doi: 10.1109/JAS.2022.105692 – ident: e_1_2_9_30_1 doi: 10.1109/TSMC.2022.3201671 – ident: e_1_2_9_35_1 doi: 10.1109/TCSI.2020.3004170 – ident: e_1_2_9_20_1 doi: 10.1002/acs.3283 – ident: e_1_2_9_36_1 doi: 10.1016/j.knosys.2022.109338 – ident: e_1_2_9_38_1 doi: 10.1109/TAC.2018.2819683 – ident: e_1_2_9_15_1 doi: 10.1016/j.ins.2021.09.027 – ident: e_1_2_9_34_1 doi: 10.1109/TFUZZ.2020.2998519 – ident: e_1_2_9_43_1 doi: 10.1016/j.nahs.2018.11.001 – volume: 415 year: 2022 ident: e_1_2_9_5_1 article-title: Event‐triggered synchronization of coupled memristive neural networks publication-title: Appl Math Comput – ident: e_1_2_9_11_1 doi: 10.1109/TNNLS.2018.2874035 – ident: e_1_2_9_32_1 doi: 10.1109/TIE.2019.2902825 – ident: e_1_2_9_22_1 doi: 10.1016/j.physa.2021.126431 – ident: e_1_2_9_25_1 doi: 10.1016/j.ins.2020.08.063 – ident: e_1_2_9_6_1 doi: 10.1016/j.jfranklin.2019.09.015 – ident: e_1_2_9_40_1 doi: 10.1016/j.jfranklin.2020.03.031 – ident: e_1_2_9_17_1 doi: 10.1109/TSMC.2021.3061768 – ident: e_1_2_9_12_1 doi: 10.1109/TNNLS.2020.3009081 – ident: e_1_2_9_19_1 doi: 10.1002/rnc.6036 – ident: e_1_2_9_4_1 doi: 10.1038/nature06932 – ident: e_1_2_9_16_1 doi: 10.1109/TSMC.2021.3071811 – ident: e_1_2_9_2_1 doi: 10.1016/j.knosys.2022.109104 – ident: e_1_2_9_41_1 doi: 10.1109/ACCESS.2021.3089374 – ident: e_1_2_9_27_1 doi: 10.1109/TASE.2022.3184022 – ident: e_1_2_9_29_1 doi: 10.1109/TNNLS.2020.3017171 |
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This work primarily concentrates on the design of fault‐tolerant anti‐disturbance control for synchronization of multiple memristor‐based neural... This work primarily concentrates on the design of fault‐tolerant anti‐disturbance control for synchronization of multiple memristor‐based neural networks... |
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SubjectTerms | Actuators Control systems design Controllers Criteria Disturbance observers Error feedback fault‐tolerant control Feedback control Linear matrix inequalities Memristors multiple disturbances multiple memristor‐based neural networks Neural networks Parameter uncertainty Performance indices Synchronism synchronization time delay and parameter uncertainties |
Title | Unified synchronization and fault‐tolerant anti‐disturbance control for synchronization of multiple memristor‐based neural networks |
URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Frnc.7112 https://www.proquest.com/docview/2921035554 |
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