×
Wang, Xuehai; Ding, Feng

Modified particle filtering-based robust estimation for a networked control system corrupted by impulsive noise. (English) Zbl 1527.93452

Int. J. Robust Nonlinear Control 32, No. 2, 830-850 (2022).
Summary: The non-Gaussian characteristic of the impulsive noise significantly degrades the performance of the \(\ell_2\)-norm-based identification algorithms. To overcome the negative effects of impulsive noise to system identification, this article develops a modified particle filtering-based robust multi-innovation gradient (MPF-MIG) algorithm for a networked control system corrupted by impulsive noise. The proposed algorithm is formulated based on a continuous logarithmic mixed \(p\)-norm cost function, which can generate an adjustable gain that adapts to the data quality. A modified particle filtering is designed to estimate the unknown true output, which is corrupted by an impulsive noise without explicit probability density function. The simulation examples exhibit that the MPF-MIG algorithm has better robustness and higher estimation accuracy than the conventional multi-innovation gradient algorithm.
{© 2021 John Wiley & Sons Ltd.}

Cited in 19 Documents

MSC:

93E11 Filtering in stochastic control theory
93E10 Estimation and detection in stochastic control theory
93B70 Networked control
93C27 Impulsive control/observation systems

Keywords:

impulsive noise; multi-innovation; nonlinear system; parameter estimation; particle filter
Cite Review PDF
Full Text: DOI

References:

[1] SimoesA, XavierJ. FADE: fast and asymptotically efficient distributed estimator for dynamic networks. IEEE Trans Signal Process. 2019;67(8):2080‐2092. · Zbl 1458.62118
[2] ZhangD, XuHL, ChenXF, ZhaiXK. Network‐based robust performance analysis for uncertain systems interconnected over an undirected graph. Int J Robust Nonlinear Control. 2021;31(1):2‐20. · Zbl 1525.93060
[3] JiY, KangZ. Three‐stage forgetting factor stochastic gradient parameter estimation methods for a class of nonlinear systems. Int J Robust Nonlinear Control. 2021;31(3):871‐987. · Zbl 1525.93438
[4] JiY, KangZ, LiuXM. The data filtering based multiple‐stage Levenberg‐Marquardt algorithm for Hammerstein nonlinear systems. Int J Robust Nonlinear Control. 2021;31(15):7007‐7025. · Zbl 1527.93457
[5] JiY, KangZ, ZhangC. Two‐stage gradient‐based recursive estimation for nonlinear models by using the data filtering. Int J Control Autom Syst. 2021;19(8):2706‐2715.
[6] HrusticE, AbdallahRB, Vilà‐VallsJ, VivetD, PagèsG, ChaumetteE. Robust linearly constrained extended Kalman filter for mismatched nonlinear systems. Int J Robust Nonlinear Control. 2021;31(3):787‐805. · Zbl 1525.93449
[7] ChengP, HeSP, StojanovicV, LuanXL, LiuF. Fuzzy fault detection for Markov jump systems with partly accessible hidden information: an event‐triggered approach. IEEE Trans Cybern. 2021. doi:10.1109/TCYB.2021.3050209
[8] TaoHF, LiJ, ChenYY, StojanovicV, YangHZ. Robust point‐to‐point iterative learning control with trial‐varying initial conditions. IET Control Theory Appl. 2020;14(19):3344‐3350.
[9] ChenJ, ZhuQM, HuMF, GuoLX, NarayanP. Improved gradient descent algorithms for time‐delay rational state‐space systems: intelligent search method and momentum method. Nonlinear Dyn. 2020;101(1):361‐373. · Zbl 1516.90117
[10] FanYM, LiuXM. Two‐stage auxiliary model gradient‐based iterative algorithm for the input nonlinear controlled autoregressive system with variable‐gain nonlinearity. Int J Robust Nonlinear Control. 2020;30(14):5492‐5509. · Zbl 1465.93041
[11] LiuXM, FanYM. Maximum likelihood extended gradient‐based estimation algorithms for the input nonlinear controlled autoregressive moving average system with variable‐gain nonlinearity. Int J Robust Nonlinear Control. 2021;31(9):4017‐4036. · Zbl 1526.93264
[12] ChenJ, HuangB, GuY. Variational Bayesian approach for ARX systems with missing observations and varying time‐delays. Automatica. 2018;94:194‐204. · Zbl 1401.93191
[13] LiMH, LiuXM. The least squares based iterative algorithms for parameter estimation of a bilinear system with autoregressive noise using the data filtering technique. Signal Process. 2018;147:23‐34.
[14] LiMH, LiuXM. Maximum likelihood hierarchical least squares‐based iterative identification for dual‐rate stochastic systems. Int J Adapt Control Signal Process. 2021;35(2):240‐261. · Zbl 1543.93353
[15] LiMH, LiuXM. Iterative identification methods for a class of bilinear systems by using the particle filtering technique. Int J Adapt Control Signal Process. 2021;35(11):2056‐2074. · Zbl 1543.93320
[16] LiMH, LiuXM. Iterative parameter estimation methods for dual‐rate sampled‐data bilinear systems by means of the data filtering technique. IET Control Theory Appl. 2021;15(9):1230‐1245.
[17] LiMH, LiuXM. Maximum likelihood least squares based iterative estimation for a class of bilinear systems using the data filtering technique. Int J Control Autom Syst. 2020;18(6):1581‐1592.
[18] JiY, ZhangC, KangZ, YuT. Parameter estimation for block‐oriented nonlinear systems using the key term separation. Int J Robust Nonlinear Control. 2020;30(9):3727‐3752. · Zbl 1466.93161
[19] DingF. Decomposition based fast least squares algorithm for output error systems. Signal Process. 2013;93(5):1235‐1242.
[20] DingF. Two‐stage least squares based iterative estimation algorithm for CARARMA system modeling. Appl Math Model. 2013;37(7):4798‐4808. · Zbl 1438.93228
[21] DingF. State filtering and parameter estimation for state space systems with scarce measurements. Signal Process. 2014;104:369‐380.
[22] PrawinJ, RamaMRA, SethiA. Parameter identification of systems with multiple disproportional local nonlinearities. Nonlinear Dyn. 2020;100(1):289‐314.
[23] ZhangQ, ShangYL, LiY, CuiNX, DuanB, ZhangCH. A novel fractional variable‐order equivalent circuit model and parameter identification of electric vehicle Li‐ion batteries. ISA Trans. 2020;97:448‐457.
[24] DuDJ, ChenR, FeiMK, LiK. A novel networked online recursive identification method for multivariable systems with incomplete measurement information. IEEE Trans Signal Inf Process Netw. 2017;3(4):744‐759.
[25] ChenJ, HuangB, ZhuQM, LiuYJ, LiL. Global convergence of the EM algorithm for ARX models with uncertain communication channels. Syst Control Lett. 2020;136:104614. · Zbl 1433.93135
[26] DuanK, FongS, Philip ChenCL. Multilayer neural networks‐based control of underwater vehicles with uncertain dynamics and disturbances. Nonlinear Dyn. 2020;100(4):3555‐3573. · Zbl 1516.93178
[27] MehmoodA, ChaudharyNI, ZameerA, RajaMAZ. Backtracking search optimization heuristics for nonlinear Hammerstein controlled auto regressive auto regressive systems. ISA Trans. 2019;91:99‐113.
[28] ZhangX, HeSP, StojanovicV, LuanXL, LiuF. Finite‐time asynchronous dissipative filtering of conic‐type nonlinear Markov jump systems. Sci China Inf Sci. 2021;64(05):151‐162.
[29] NakabayashiA, UenoG. Nonlinear filtering method using a switching error model for outlier‐contaminated observations. IEEE Trans Automat Contr. 2020;65(7):3150‐3156. · Zbl 1533.93811
[30] LiWS, WangZD, YuanY, GuoL. Two‐stage particle filtering for non‐Gaussian state estimation with fading measurements. Automatica. 2020;115:108882. · Zbl 1436.93139
[31] StojanovicV, PrsicD. Robust identification for fault detection in the presence of non‐Gaussian noises: application to hydraulic servo drives. Nonlinear Dyn. 2020;100(3):2299‐2313. · Zbl 1516.93031
[32] RahmaniMR, FarrokhiM. Robust identification of MISO neuro‐fractional‐order Hammerstein systems. Int J Robust Nonlinear Control. 2019;29(7):2137‐2148. · Zbl 1418.93061
[33] YangXQ, LiuX, YinS. Robust identification of nonlinear systems with missing observations: the case of state‐space model structure. IEEE Trans Ind Inf. 2019;15(5):2763‐2774.
[34] StojanovicV, HeSP, ZhangBY. State and parameter joint estimation of linear stochastic systems in presence of faults and non‐Gaussian noises. Int J Robust Nonlinear Control. 2020;30(16):6683‐6700. · Zbl 1525.93440
[35] ZayyaniH. Continuous mixed p‐norm adaptive algorithm for system identification. IEEE Signal Process Lett. 2014;21(9):1108‐1110.
[36] YanLP, DiCY, Jonathan WuQM, XiaYQ, LiuSD. Distributed fusion estimation for multisensor systems with non‐Gaussian but heavy‐tailed noises. ISA Trans. 2020;101:160‐169.
[37] WangXH, DingF. The robust multi‐innovation estimation algorithm for Hammerstein nonlinear systems with non‐Gaussian noise. IET Control Theory Appl. 2021;15(7):989‐1002.
[38] BaiEW. Source localization by a binary sensor network in the presence of imperfection, noise, and outliers. IEEE Trans Automat Contr. 2018;63(2):347‐359. · Zbl 1390.93743
[39] ChenJ, LiuYJ, DingF, ZhuQ. Gradient‐based particle filter algorithm for an ARX model with nonlinear communication output. IEEE Trans Syst Man Cybern‐Syst. 2020;50(6):2198‐2207.
[40] LuL, ZhaoHQ, ChenBD. Robust adaptive algorithm for smart antenna system with alpha‐stable noise. IEEE Trans Circuits Syst. 2018;65(11):1783‐1787.
[41] LjungL. System Identification: Theory for the User. 2nd ed.Prentice Hall; 1999.
[42] DingF, XuL, ZhuQM. Performance analysis of the generalized projection identification for time‐varying systems. IET Control Theory Appl. 2016;10(18):2506‐2514.
[43] ArulampalamMS, MaskellS, GordonN, ClappT. A tutorial on particle filters for online nonlinear/non‐Gaussian Bayesian tracking. IEEE Trans Signal Process. 2002;50:174‐188.
[44] SamoradnitskyG. Stable non‐Gaussian Random Processes: Stochastic Models with Infinite Variance. Routledge; 2017.
[45] PanJ, JiangX, WanXK, DingW. A filtering based multi‐innovation extended stochastic gradient algorithm for multivariable control systems. Int J Control Autom Syst. 2017;15(3):1189‐1197.
[46] PanJ, MaH, ZhangX, et al. Recursive coupled projection algorithms for multivariable output‐error‐like systems with coloured noises. IET Signal Process. 2020;14(7):455‐466.
[47] MaH, ZhangX, LiuQY, et al. Partiallly‐coupled gradient‐based iterative algorithms for multivariable output‐error‐like systems with autoregressive moving average noises. IET Control Theory Appl. 2020;14(17):2613‐2627. · Zbl 1542.93385
[48] MaH, PanJ. Partially‐coupled least squares based iterative parameter estimation for multi‐variable output‐error‐like autoregressive moving average systems. IET Control Theory Appl. 2019;13(18):3040‐3051.
[49] MaP, WangL. Filtering‐based recursive least squares estimation approaches for multivariate equation‐error systems by using the multi‐innovation theory. Int J Adapt Control Signal Process. 2021;35(9):1898‐1915. · Zbl 1536.93999
[50] DingJL, ZhangWH. Finite‐time adaptive control for nonlinear systems with uncertain parameters based on the command filters. Int J Adapt Control Signal Process. 2021;35(9):1754‐1767. · Zbl 1543.93146
[51] ChenJ, ZhuQM, LiuYJ. Modified Kalman filtering based multi‐step‐length gradient iterative algorithm for ARX models with random missing outputs. Automatica. 2020;118:109034. · Zbl 1447.93350
[52] XuL, ChenL, XiongWL. Parameter estimation and controller design for dynamic systems from the step responses based on the Newton iteration. Nonlinear Dyn. 2015;79(3):2155‐2163.
[53] XuL. The damping iterative parameter identification method for dynamical systems based on the sine signal measurement. Signal Process. 2016;120:660‐667.
[54] XuL, ZhuQ. Decomposition strategy‐based hierarchical least mean square algorithm for control systems from the impulse responses. Int J Syst Sci. 2021;52(9):1806‐1821. · Zbl 1483.93637
[55] XuL, YangEF. Auxiliary model multi‐innovation stochastic gradient parameter estimation methods for nonlinear sandwich systems. Int J Robust Nonlinear Control. 2021;31(1):148‐165. · Zbl 1525.93043
[56] ChenJ, HuangB, GanM, ChenCLP. A novel reduced‐order algorithm for rational models based on Arnoldi process and Krylov subspace. Automatica. 2021;129:109663. · Zbl 1478.93081
[57] MaoYW, LiuS, LiuJF. Robust economic model predictive control of nonlinear networked control systems with communication delays. Int J Adapt Control Signal Process. 2020;34(5):614‐637. · Zbl 1467.93095
[58] ChenJ, ShenQY, MaJX, LiuYJ. Stochastic average gradient algorithm for multirate FIR models with varying time delays using self‐organizing maps. Int J Adapt Control Signal Process. 2020;34(7):955‐970. · Zbl 1469.93108
[59] XiongW, JiaX, YangD, et al. DP‐LinkNet: a convolutional network for historical document image binarization. KSII Trans Internet Inf Syst. 2021;15(5):1778‐1797.
[60] XiongW, ZhouL, YueL, et al. An enhanced binarization framework for degraded historical document images. EURASIP J Image Video Process. 2021;2021:13.
[61] ChangC, WangQY, JiangJC, WuTZ. Lithium‐ion battery state of health estimation using the incremental capacity and wavelet neural networks with genetic algorithm. J Energy Storage. 2021;38:102570.
[62] WangGH, ZhangTL, DaiYY, et al. A serial‐parallel self‐attention network joint with multi‐scale dilated convolution. IEEE Access. 2021;9(5):71909‐71919.
[63] KongJL, WangHX, WangXY, et al. Multi‐stream hybrid architecture based on cross‐level fusion strategy for fine‐grained crop species recognition in precision agriculture. Comput Electron Agric. 2021;185:106134.
[64] JinXB, ZhangWZ, KongJL, et al. Deep‐learning forecasting method for electric power load via attention‐based encoder‐decoder with Bayesian optimization. Energies. 2021;14(6):1596.
[65] ZhengYY, KongJL, JinXB, WangXY, SuTL, ZuoM. CropDeep: the crop vision dataset for deep‐learning‐based classification and detection in precision agriculture. Sensors. 2019;19(5):1058.
[66] ZhangX. Adaptive parameter estimation for a general dynamical system with unknown states. Int J Robust Nonlinear Control. 2020;30(4):1351‐1372. · Zbl 1465.93115
[67] ZhangX. Recursive parameter estimation methods and convergence analysis for a special class of nonlinear systems. Int J Robust Nonlinear Control. 2020;30(4):1373‐1393. · Zbl 1465.93218
[68] ZhangX. Recursive parameter estimation and its convergence for bilinear systems. IET Control Theory Appl. 2020;14(5):677‐688. · Zbl 07907139
[69] ZhangX. Hierarchical parameter and state estimation for bilinear systems. Int J Syst Sci. 2020;51(2):275‐290. · Zbl 1483.93677
[70] ZhangX, HayatT. Recursive identification of bilinear time‐delay systems through the redundant rule. J Franklin Inst. 2020;357(1):726‐747. · Zbl 1429.93401
[71] WangJW, JiY, ZhangC. Iterative parameter and order identification for fractional‐order nonlinear finite impulse response systems using the key term separation. Int J Adapt Control Signal Process. 2021;35(8):1562‐1577. · Zbl 1543.93052
[72] DingF, LiuG, LiuXP. Parameter estimation with scarce measurements. Automatica. 2011;47(8):1646‐1655. · Zbl 1232.62043
[73] DingF, LiuYJ, BaoB. Gradient based and least squares based iterative estimation algorithms for multi‐input multi‐output systems. Proc Inst Mech Eng Part I: J Syst Control Eng. 2012;226(1):43‐55.
[74] XuL, XiongWL, AlsaediA, HayatT. Hierarchical parameter estimation for the frequency response based on the dynamical window data. Int J Control Autom Syst. 2018;16(4):1756‐1764.
[75] XuL, SongGL. A recursive parameter estimation algorithm for modeling signals with multi‐frequencies. Circuits Syst Signal Process. 2020;39(8):4198‐4224. · Zbl 1452.94026
[76] PanJ, LiW, ZhangHP. Control algorithms of magnetic suspension systems based on the improved double exponential reaching law of sliding mode control. Int J Control Autom Syst. 2018;16(6):2878‐2887.
[77] CaoY, SunYK, XieG, LiP. A sound‐based fault diagnosis method for railway point machines based on two‐stage feature selection strategy and ensemble classifier. IEEE Trans Intell Transp Syst. 2022. doi:10.1109/TITS.2021.3109632
[78] CaoY, MaLC, XiaoS, et al. Standard analysis for transfer delay in CTCS‐3. Chin J Electron. 2017;26(5):1057‐1063.
[79] CaoY, WenJK, MaLC. Tracking and collision avoidance of virtual coupling train control system. Alex Eng J. 2021;60(2):2115‐2125.
[80] CaoY, WenJK, MaLC. Tracking and collision avoidance of virtual coupling train control system. Futur Gener Comput Syst. 2021;120:76‐90.
[81] SuS, WangXK, CaoY, YinJT. An energy‐efficient train operation approach by integrating the metro timetabling and eco‐driving. IEEE Trans Intell Transp Syst. 2020;21(10):4252‐4268.
[82] SuS, TangT, XunJ, CaoF, WangYH. Design of running grades for energy‐efficient train regulation: a case study for Beijing Yizhuang line. IEEE Intell Transp Syst Mag. 2021;13(2):189‐200.
[83] CaoY, WangZ, LiuF, et al. Bio‐inspired speed curve optimization and sliding mode tracking control for subway trains. IEEE Trans Veh Technol. 2019;68(7):6331‐6342.
[84] CaoY, SunYK, XieG, et al. Fault diagnosis of train plug door based on a hybrid criterion for IMFs selection and fractional wavelet package energy entropy. IEEE Trans Veh Technol. 2019;68(8):7544‐7551.
[85] ZhaoZY, WangXY, YaoP, BaiYT. A health performance evaluation method of multirotors under wind turbulence. Nonlinear Dyn. 2020;102(3):1701‐1715.
[86] ZhangX, YangEF. Highly computationally efficient state filter based on the delta operator. Int J Adapt Control Signal Process. 2019;33(6):875‐889. · Zbl 1425.93290
[87] ZhangX, YangEF. State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors. Int J Adapt Control Signal Process. 2019;33(7):1157‐1173. · Zbl 1425.93278
[88] XuL, ShengJ. Separable multi‐innovation stochastic gradient estimation algorithm for the nonlinear dynamic responses of systems. Int J Adapt Control Signal Process. 2020;34(7):937‐954. · Zbl 1469.93111
[89] XuL, HayatT. Hierarchical recursive signal modeling for multi‐frequency signals based on discrete measured data. Int J Adapt Control Signal Process. 2021;35(5):676‐693. · Zbl 1543.93331
[90] ZhangX, YangEF. State filtering‐based least squares parameter estimation for bilinear systems using the hierarchical identification principle. IET Control Theory Appl. 2018;12(12):1704‐1713.
[91] ZhangX, HayatT. Combined state and parameter estimation for a bilinear state space system with moving average noise. J Franklin Inst. 2018;355(6):3079‐3103. · Zbl 1395.93174
[92] ZhangX, HayatT. Recursive parameter identification of the dynamical models for bilinear state space systems. Nonlinear Dyn. 2017;89(4):2415‐2429. · Zbl 1377.93062
[93] WangLJ, GuoJ, XuC, et al. Hybrid model predictive control strategy of supercapacitor energy storage system based on double active bridge. Energies. 2019;12(11):2134.
[94] ZhangY, YanZ, ZhouCC, et al. Capacity allocation of HESS in micro‐grid based on ABC algorithm. Int J Low‐Carbon Technol. 2020;15(4):496‐505.
[95] ZhangX, HayatT. A hierarchical approach for joint parameter and state estimation of a bilinear system with autoregressive noise. Mathematika. 2019;7(4):356.
[96] HanHG, LiuHX, LiJM, QiaoJF. Cooperative fuzzy‐neural control for wastewater treatment process. IEEE Trans Ind Inf. 2021;17(9):5971‐5981.
[97] HanHG, LiuZ, LiuHX, QiaoJF. Knowledge‐data‐driven model predictive control for a class of nonlinear systems. IEEE Trans Syst Man Cyber‐Syst. 2021;51(7):4492‐4504.
[98] HanHG, ZhangL, KiuHX, YangCL, QiaoJF. Intelligent optimal control system with flexible objective functions and its applications in wastewater treatment process. IEEE Trans Syst Man Cybern‐Syst. 2021;51(6):3464‐3476.
[99] BuN, PangJX, DengM. Robust fault tolerant tracking control for the multi‐joint manipulator based on operator theory. J Franklin Inst. 2020;357(5):2696‐2714. · Zbl 1451.93068
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.