Finite-time energy-to-peak quantized filtering for Markov jump networked systems under weighted try-once-discard protocol. (English) Zbl 1525.93458
Summary: This work addresses the finite-time energy-to-peak filtering issue for networked systems subject to quantization effects and the weighted try-once-discard (WTOD) protocol, in which the random changes of coupling connections are governed by a Markov chain. To avoid the conflict in data transmission, the WTOD protocol is introduced to the communication between the sensor node and the filter, which guarantees that just one sensor node has access to send data at each transmission instant. In addition, the quantizer is employed to improve the reliability of data transmission. The objective of this article is to construct a filter such that the filtering error system satisfies the requirements of both the finite-time boundary and the specified energy-to-peak performance. Based on the Kronecker product, the Lyapunov stability theory and an improved matrix decoupling method, some sufficient conditions are established through disposing of convex optimization issues. Ultimately, the availability of the designed filter is illustrated via a numerical example.
{© 2021 John Wiley & Sons Ltd.}
{© 2021 John Wiley & Sons Ltd.}
MSC:
| 93E11 | Filtering in stochastic control theory |
| 93B70 | Networked control |
| 93D40 | Finite-time stability |
Keywords:
energy-to-peak filtering; finite-time boundary; Markov jump networked systems; weighted try-once-discard protocolReferences:
| [1] | WangZ, BauchCT, BhattacharyyaS, et al. Statistical physics of vaccination. Phys Rep. 2016;664:1‐113. · Zbl 1359.92111 |
| [2] | SuY, HuangJ. Two consensus problems for discrete‐time multi‐agent systems with switching network topology. Automatica. 2012;48(9):1988‐1997. · Zbl 1258.93015 |
| [3] | LeeT‐C, TanY, SuY, MareelsI. Invariance principles and observability in switched systems with an application in consensus. IEEE Trans Autom Control. 2020. https://doi.org/10.1109/TAC.2020.3035594. · Zbl 1536.93802 · doi:10.1109/TAC.2020.3035594 |
| [4] | WangJ, HuangZ, WuZ, CaoJ, ShenH. Extended dissipative control for singularly perturbed PDT switched systems and its application. IEEE Trans Circuits Syst I Reg Papers. 2020;67(12):5281‐5289. · Zbl 1468.93116 |
| [5] | WuY, LuR, ShiP, SuH, WuZ‐G. Adaptive output synchronization of heterogeneous network with an uncertain leader. Automatica. 2017;76:183‐192. · Zbl 1352.93013 |
| [6] | ZhouJ, CaiT, ZhouW, TongD. Master‐slave synchronization for coupled neural networks with Markovian switching topologies and stochastic perturbation. Int J Robust Nonlinear Control. 2018;28(6):2249‐2263. · Zbl 1390.93012 |
| [7] | KarimiHR, GaoH. New delay‐dependent exponential H_∞ synchronization for uncertain neural networks with mixed time delays. IEEE Trans Syst Man Cybernet Part B. 2010;40(1):173‐185. |
| [8] | ZhangH, QiuZ, LiuX, XiongL. Stochastic robust finite‐time boundedness for semi‐Markov jump uncertain neutral‐type neural networks with mixed time‐varying delays via a generalized reciprocally convex combination inequality. Int J Robust Nonlinear Control. 2020;30(5):2001‐2019. · Zbl 1465.93227 |
| [9] | WuY‐Q, SuH, LuR, WuZ‐G, ShuZ. Passivity‐based non‐fragile control for Markovian jump systems with aperiodic sampling. Syst Control Lett. 2015;84:35‐43. · Zbl 1326.93082 |
| [10] | MahmoudMS, ShiP. Robust stability, stabilization and \(\mathcal{\mathcal{H}}_\infty\) control of time‐delay systems with Markovian jump parameters. Int J Robust Nonlinear Control. 2003;13(8):755‐784. · Zbl 1029.93063 |
| [11] | LiuT, HillDJ, ZhaoJ. Synchronization of dynamical networks by network control. IEEE Trans Autom Control. 2012;57(6):1574‐1580. · Zbl 1369.93034 |
| [12] | DongS, WuZ, ShiP, SuH, HuangT. Quantized control of Markov jump nonlinear systems based on fuzzy hidden Markov model. IEEE Trans Cybern. 2019;49(7):2420‐2430. |
| [13] | DongS, WuZ‐G, PanY‐J, SuH, LiuY. Hidden‐Markov‐model‐based asynchronous filter design of nonlinear Markov jump systems in continuous‐time domain. IEEE Trans Cybern. 2019;49(6):2294‐2304. |
| [14] | ShenH, DaiM, LuoY, CaoJ, ChadliM. Fault‐tolerant fuzzy control for semi‐Markov jump nonlinear systems subject to incomplete SMK and actuator failures. IEEE Trans Fuzzy Syst. 2020;1. https://doi.org/10.1109/TFUZZ.2020.3011760. · doi:10.1109/TFUZZ.2020.3011760 |
| [15] | LiuY‐A, XiaJ, MengB, SongX, ShenH. Extended dissipative synchronization for semi‐markov jump complex dynamic networks via memory sampled‐data control scheme. J Frankl Inst. 2020;357(15):10900‐10920. · Zbl 1450.93082 |
| [16] | WangJ, XiaJ, ShenH, XingM, ParkJH. H∞ synchronization for fuzzy Markov jump chaotic systems with piecewise‐constant transition probabilities subject to PDT switching rule. IEEE Trans Fuzzy Syst. 2020;1. https://doi.org/10.1109/TFUZZ.2020.3012761. · doi:10.1109/TFUZZ.2020.3012761 |
| [17] | JiangB, KarimiHR. Further criterion for stochastic stability analysis of semi‐Markovian jump linear systems. Int J Robust Nonlinear Control. 2020;30(7):2689‐2700. · Zbl 1465.93224 |
| [18] | WangY, HuX, ShiK, SongX, ShenH. Network‐based passive estimation for switched complex dynamical networks under persistent dwell‐time with limited signals. J Frankl Inst. 2020;357(15):10921‐10936. · Zbl 1450.93078 |
| [19] | ShenH, XingM, WuZ, CaoJ, HuangT. l_2−l_∞ state estimation for persistent dwell‐time switched coupled networks subject to round‐robin protocol. IEEE Trans Neural Netw Learn Syst. 2020;1‐13. https://doi.org/10.1109/TNNLS.2020.2995708. · doi:10.1109/TNNLS.2020.2995708 |
| [20] | YangX, LuJ. Finite‐time synchronization of coupled networks with Markovian topology and impulsive effects. IEEE Trans Autom Control. 2016;61(8):2256‐2261. · Zbl 1359.93459 |
| [21] | LiuK, FridmanE, XiaY. Networked Control Under Communication Constraints: A Time‐Delay Approach. Vol 11. Berlin, Germany: Springer Nature; 2020. · Zbl 1458.93002 |
| [22] | LiY, LiuK, HeW, YinY, JohanssonR, ZhangK. Bilateral teleoperation of multiple robots under scheduling communication. IEEE Trans Control Syst Technol. 2020;28(5):1770‐1784. |
| [23] | WangJ, WangY, YanH, CaoJ, ShenH. Hybrid event‐based leader‐following consensus of nonlinear multi‐agent systems with semi‐Markov jump parameters. IEEE Syst J. 2020;1‐12. https://doi.org/10.1109/JSYST.2020.3029156. · doi:10.1109/JSYST.2020.3029156 |
| [24] | DongS, SuH, ShiP, LuR, WuZ‐G. Filtering for discrete‐time switched fuzzy systems with quantization. IEEE Trans Fuzzy Syst. 2017;25(6):1616‐1628. |
| [25] | OliveiraAM, CostaO. An iterative approach for the discrete‐time dynamic control of Markov jump linear systems with partial information. Int J Robust Nonlinear Control. 2020;30(2):495‐511. · Zbl 1440.93242 |
| [26] | XieW, ZhangR, ZengD, ShiK, ZhongS. Strictly dissipative stabilization of multiple‐memory Markov jump systems with general transition rates: a novel event‐triggered control strategy. Int J Robust Nonlinear Control. 2020;30(5):1956‐1978. · Zbl 1465.93225 |
| [27] | WangZ, SunJ, ChenJ, BaiY. Finite‐time stability of switched nonlinear time‐delay systems. Int J Robust Nonlinear Control. 2020;30(7):2906‐2919. · Zbl 1465.93190 |
| [28] | RuT, XiaJ, HuangX, ChengX, WangJ. Reachable set estimation of delayed fuzzy inertial neural networks with Markov jumping parameters. J Frankl Inst. 2020;357(11):6882‐6898. · Zbl 1447.93020 |
| [29] | ShenH, ParkJH, WuZ‐G, ZhangZ. Finite‐time H_∞ synchronization for complex networks with semi‐Markov jump topology. Commun Nonlinear Sci Numer Simul. 2015;24(1‐3):40‐51. · Zbl 1440.93074 |
| [30] | SongX, WangZ, ShenH, LiF, ChenB, LuJ. A unified method to energy‐to‐peak filter design for networked Markov switched singular systems over a finite‐time interval. J Frankl Inst. 2017;354(17):7899‐7916. · Zbl 1380.93258 |
| [31] | ZhangL, ZhuY, ShiP, ZhaoY. Resilient asynchronous H_∞ filtering for Markov jump neural networks with unideal measurements and multiplicative noises. IEEE Trans Cybern. 2015;45(12):2840‐2852. |
| [32] | MaR, ShiP, WangZ, WuL. Resilient filtering for cyber‐physical systems under denial‐of‐service attacks. Int J Robust Nonlinear Control. 2020;30(5):1754‐1769. · Zbl 1465.93215 |
| [33] | ZouL, WangZ, GaoH. Set‐membership filtering for time‐varying systems with mixed time‐delays under round‐robin and weighted try‐once‐discard protocols. Automatica. 2016;74:341‐348. · Zbl 1348.93265 |
| [34] | ZouL, WangZ, HanQ‐L, ZhouD. Ultimate boundedness control for networked systems with try‐once‐discard protocol and uniform quantization effects. IEEE Trans Autom Control. 2017;62(12):6582‐6588. · Zbl 1390.93521 |
| [35] | LiuM, HoDW, NiuY. Robust filtering design for stochastic system with mode‐dependent output quantization. IEEE Trans Signal Proc. 2010;58(12):6410‐6416. · Zbl 1391.93248 |
| [36] | GaidMEMB, ÇelaA. Trading quantization precision for update rates for systems with limited communication in the uplink channel. Automatica. 2010;46(7):1210‐1214. · Zbl 1194.93122 |
| [37] | ZongG, WangR, ZhengW, HouL. Finite‐time H_∞ control for discrete‐time switched nonlinear systems with time delay. Int J Robust Nonlinear Control. 2015;25(6):914‐936. · Zbl 1309.93057 |
| [38] | LiangJ, WangZ, LiuY, LiuX. Robust synchronization of an array of coupled stochastic discrete‐time delayed neural networks. IEEE Trans Neural Netw. 2008;19(11):1910‐1921. |
| [39] | ShenH, HuoS, CaoJ, HuangT. Generalized state estimation for Markovian coupled networks under round‐robin protocol and redundant channels. IEEE Trans Cybern. 2019;49(4):1292‐1301. |
| [40] | LiuK, FridmanE. Discrete‐time network‐based control under try‐once‐discard protocol and actuator constraints. Paper presented at: Proceedings of the European Control Conference; 2014:442‐447; Strasbourg, France. |
| [41] | ZhangJ, PengC. Networked H_∞ filtering under a weighted TOD protocol. Automatica. 2019;107:333‐341. · Zbl 1429.93397 |
| [42] | WalshGC, YeH, BushnellLG. Stability analysis of networked control systems. IEEE Trans Control Syst Technol. 2002;10(3):438‐446. |
| [43] | ShenH, LiF, YanH, KarimiHR, LamH‐K. Finite‐time event‐triggered \(\mathcal{\mathcal{H}}_\infty\) control for T‐S fuzzy Markov jump systems. IEEE Trans Fuzzy Syst. 2018;26(5):3122‐3135. |
| [44] | XuY, LuR, TaoJ, PengH, XieK. Nonfragile l_2 − l_∞ state estimation for discrete‐time neural networks with jumping saturations. Neurocomputing. 2016;207:15‐21. |
| [45] | ShenB, WangZ, DingD, ShuH. H_∞ state estimation for complex networks with uncertain inner coupling and incomplete measurements. IEEE Trans Neural Netw Learn Syst. 2013;24(12):2027‐2037. |
| [46] | ZouL, WangZ, GaoH, LiuX. State estimation for discrete‐time dynamical networks with time‐varying delays and stochastic disturbances under the round‐robin protocol. IEEE Trans Neural Netw Learn Syst. 2017;28(5):1139‐1151. |
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