Online state and unknown inputs estimation for nonlinear systems with particle filter based recursive expectation‐maximization algorithm
The article presents an innovative approach to simultaneously estimate states and unknown inputs (UIs) in nonlinear systems using a particle filter (PF) based recursive expectation‐maximization (EM) algorithm. This method is distinct from traditional iterative EM algorithms. During the E‐step, it ca...
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| Vydáno v: | International journal of robust and nonlinear control Ročník 34; číslo 13; s. 8768 - 8784 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Bognor Regis
Wiley Subscription Services, Inc
10.09.2024
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| ISSN: | 1049-8923, 1099-1239 |
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| Abstract | The article presents an innovative approach to simultaneously estimate states and unknown inputs (UIs) in nonlinear systems using a particle filter (PF) based recursive expectation‐maximization (EM) algorithm. This method is distinct from traditional iterative EM algorithms. During the E‐step, it calculates the Q‐function recursively within the maximum likelihood framework, while the PF estimates the system states. The M‐step involves local maximization of the recursive Q‐function to online estimate the UIs. The effectiveness of the PF‐based recursive EM algorithm is demonstrated with a numerical example, and comparisons with the augmented state PF are made to highlight its advantages. Finally, the proposed algorithm is implemented in a real application for the estimation of the continuous fermentation process. |
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| AbstractList | The article presents an innovative approach to simultaneously estimate states and unknown inputs (UIs) in nonlinear systems using a particle filter (PF) based recursive expectation‐maximization (EM) algorithm. This method is distinct from traditional iterative EM algorithms. During the E‐step, it calculates the Q‐function recursively within the maximum likelihood framework, while the PF estimates the system states. The M‐step involves local maximization of the recursive Q‐function to online estimate the UIs. The effectiveness of the PF‐based recursive EM algorithm is demonstrated with a numerical example, and comparisons with the augmented state PF are made to highlight its advantages. Finally, the proposed algorithm is implemented in a real application for the estimation of the continuous fermentation process. |
| Author | Liu, Zhuangyu Zhao, Shunyi Wan, Haiying Liu, Fei Luan, Xiaoli |
| Author_xml | – sequence: 1 givenname: Zhuangyu surname: Liu fullname: Liu, Zhuangyu organization: Jiangnan University – sequence: 2 givenname: Shunyi surname: Zhao fullname: Zhao, Shunyi organization: Jiangnan University – sequence: 3 givenname: Haiying surname: Wan fullname: Wan, Haiying organization: Jiangnan University – sequence: 4 givenname: Xiaoli surname: Luan fullname: Luan, Xiaoli email: xlluan@jiangnan.edu.cn organization: Jiangnan University – sequence: 5 givenname: Fei orcidid: 0000-0001-7160-2605 surname: Liu fullname: Liu, Fei organization: Jiangnan University |
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| Cites_doi | 10.1002/cjce.20113 10.1016/j.automatica.2022.110365 10.1016/j.ress.2023.109416 10.1016/j.sigpro.2019.03.004 10.1016/j.compchemeng.2013.03.024 10.1002/rnc.3674 10.1016/j.chemolab.2021.104403 10.1162/neco.1994.6.2.181 10.1109/TII.2021.3057421 10.1016/j.inffus.2022.03.004 10.1109/TAC.2021.3061993 10.1109/TSP.2007.907883 10.1109/TAC.2017.2681520 10.1016/S0005-1098(00)00089-3 10.1002/0470045345 10.1109/ICIEA.2012.6360967 10.1109/CDC.2016.7799365 10.1111/j.2517-6161.1977.tb01600.x 10.1109/TAC.2015.2514259 10.1007/s11071-014-1754-x 10.1002/rnc.5787 10.2307/2983440 10.1016/j.automatica.2009.04.009 10.1109/ACC.2006.1655461 10.1021/acs.iecr.8b06091 10.1016/j.automatica.2019.02.050 10.1016/j.conengprac.2023.105650 10.1002/rnc.1190 10.1016/j.ymssp.2019.03.013 10.1021/acs.iecr.0c03793 10.1016/j.sigpro.2013.12.032 |
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| References_xml | – volume: 27 start-page: 2167 issue: 13 year: 2017 end-page: 2197 article-title: EM‐based adaptive divided difference filter for nonlinear system with multiplicative parameter publication-title: Int J Robust Nonlinear Control – volume: 39 start-page: 1 issue: 1 year: 1977 end-page: 22 article-title: Maximum likelihood from incomplete data via the EM algorithm publication-title: J R Stat Soc B Methodol – volume: c60 start-page: 2971 issue: 7 year: 2021 end-page: 2982 article-title: Simultaneous state and parameter estimation: The role of sensitivity analysis publication-title: Ind Eng Chem Res – volume: 62 start-page: 5497 issue: 11 year: 2017 end-page: 5510 article-title: State and unknown input observers for nonlinear systems with bounded exogenous inputs publication-title: IEEE Trans Automat Contr – volume: 31 start-page: 9519 issue: 18 year: 2021 end-page: 9537 article-title: Joint estimation of state, parameter, and unknown input for nonlinear systems: a composite estimation scheme publication-title: Int J Robust Nonlinear Control – volume: 17 start-page: 1734 issue: 18 year: 2007 end-page: 1753 article-title: Exact state estimation for linear systems with unknown inputs based on hierarchical super‐twisting algorithm publication-title: Int J Robust Nonlinear Control – volume: 217 year: 2021 article-title: Online state and inputs identification for stochastic systems using recursive expectation‐maximization algorithm publication-title: Chemom Intel Lab Syst – volume: 58 start-page: 11553 issue: 26 year: 2019 end-page: 11565 article-title: Simultaneous estimation of hidden state and unknown input using expectation maximization algorithm publication-title: Ind Eng Chem Res – volume: 61 start-page: 4210 issue: 12 year: 2016 end-page: 4216 article-title: Measurement random latency probability identification publication-title: IEEE Trans Automat Contr – volume: 238 start-page: 109416 year: 2023 article-title: Digital twins‐based process monitoring for wastewater treatment processes publication-title: Reliab Eng Syst Saf – volume: 142 year: 2022 article-title: Expectation–maximization algorithm for bilinear systems by using the Rauch–Tung–Striebel smoother publication-title: Automatica – volume: 104 start-page: 57 year: 2019 end-page: 66 article-title: Estimating unbounded unknown inputs in nonlinear systems publication-title: Automatica – start-page: 1533 year: 2012 end-page: 1538 – volume: 79 start-page: 1469 year: 2015 end-page: 1479 article-title: Unknown input observer design for one‐sided Lipschitz nonlinear systems publication-title: Nonlinear Dyn – volume: 57 start-page: 159 year: 2013 end-page: 172 article-title: Parameter estimation in batch process using EM algorithm with particle filter publication-title: Comput Chem Eng – volume: 36 start-page: 1627 issue: 11 year: 2000 end-page: 1638 article-title: New developments in state estimation for nonlinear systems publication-title: Automatica – volume: 161 start-page: 268 year: 2019 end-page: 288 article-title: Optimal joint estimation and identification theorem to linear Gaussian system with unknown inputs publication-title: Signal Process – volume: 85 start-page: 23 year: 2022 end-page: 51 article-title: Nonlinear unknown input observability and unknown input reconstruction: the general analytical solution publication-title: Inform Fusion – volume: 86 start-page: 1081 issue: 6 year: 2008 end-page: 1092 article-title: A particle filter approach to identification of nonlinear processes under missing observations publication-title: Can J Chem Eng – year: 2006 – volume: 6 start-page: 181 issue: 2 year: 1994 end-page: 214 article-title: Hierarchical mixtures of experts and the EM algorithm publication-title: Neural Comput Ind Eng Chem Res – start-page: 7111 year: 2016 end-page: 7116 – volume: 139 start-page: 105650 year: 2023 article-title: Dynamic multi‐objective optimization and multi‐units linear active disturbance rejection control for wastewater treatment processes publication-title: Control Eng Pract – volume: 56 start-page: 921 issue: 3 year: 2008 end-page: 936 article-title: An EM algorithm for nonlinear state estimation with model uncertainties publication-title: IEEE Trans Signal Process – volume: 66 start-page: 6115 issue: 12 year: 2021 end-page: 6122 article-title: Simultaneous estimation of the state, unknown input, and output disturbance in discrete‐time linear systems publication-title: IEEE Trans Automat Contr – volume: 127 start-page: 120 year: 2019 end-page: 135 article-title: A novel unscented Kalman filter for recursive state‐input‐system identification of nonlinear systems publication-title: Mech Syst Signal Process – volume: 17 start-page: 8429 issue: 12 year: 2021 end-page: 8437 article-title: Intelligent state estimation for continuous fermenters using variational Bayesian learning publication-title: IEEE Trans Industr Inform – start-page: 5 year: 2006 – volume: 158 start-page: 419 issue: 3 year: 1995 end-page: 444 article-title: Model uncertainty, data mining and statistical inference publication-title: J R Stat Soc Ser A Stat Soc – volume: 45 start-page: 1970 issue: 8 year: 2009 end-page: 1974 article-title: An observation algorithm for nonlinear systems with unknown inputs publication-title: Automatica – volume: 99 start-page: 171 year: 2014 end-page: 184 article-title: Recursive hidden input estimation in nonlinear dynamic systems with varying amounts of a priori knowledge publication-title: Signal Process – ident: e_1_2_10_24_1 doi: 10.1002/cjce.20113 – ident: e_1_2_10_29_1 doi: 10.1016/j.automatica.2022.110365 – ident: e_1_2_10_3_1 doi: 10.1016/j.ress.2023.109416 – ident: e_1_2_10_8_1 doi: 10.1016/j.sigpro.2019.03.004 – ident: e_1_2_10_25_1 doi: 10.1016/j.compchemeng.2013.03.024 – ident: e_1_2_10_27_1 doi: 10.1002/rnc.3674 – ident: e_1_2_10_10_1 doi: 10.1016/j.chemolab.2021.104403 – ident: e_1_2_10_30_1 doi: 10.1162/neco.1994.6.2.181 – ident: e_1_2_10_32_1 doi: 10.1109/TII.2021.3057421 – ident: e_1_2_10_20_1 doi: 10.1016/j.inffus.2022.03.004 – ident: e_1_2_10_9_1 doi: 10.1109/TAC.2021.3061993 – ident: e_1_2_10_23_1 doi: 10.1109/TSP.2007.907883 – ident: e_1_2_10_14_1 doi: 10.1109/TAC.2017.2681520 – ident: e_1_2_10_5_1 doi: 10.1016/S0005-1098(00)00089-3 – ident: e_1_2_10_2_1 doi: 10.1002/0470045345 – ident: e_1_2_10_16_1 doi: 10.1109/ICIEA.2012.6360967 – ident: e_1_2_10_13_1 doi: 10.1109/CDC.2016.7799365 – ident: e_1_2_10_22_1 doi: 10.1111/j.2517-6161.1977.tb01600.x – ident: e_1_2_10_28_1 doi: 10.1109/TAC.2015.2514259 – ident: e_1_2_10_12_1 doi: 10.1007/s11071-014-1754-x – ident: e_1_2_10_19_1 doi: 10.1002/rnc.5787 – ident: e_1_2_10_6_1 doi: 10.2307/2983440 – ident: e_1_2_10_15_1 doi: 10.1016/j.automatica.2009.04.009 – ident: e_1_2_10_11_1 doi: 10.1109/ACC.2006.1655461 – ident: e_1_2_10_21_1 doi: 10.1021/acs.iecr.8b06091 – ident: e_1_2_10_17_1 doi: 10.1016/j.automatica.2019.02.050 – ident: e_1_2_10_4_1 doi: 10.1016/j.conengprac.2023.105650 – ident: e_1_2_10_7_1 doi: 10.1002/rnc.1190 – ident: e_1_2_10_18_1 doi: 10.1016/j.ymssp.2019.03.013 – ident: e_1_2_10_31_1 doi: 10.1021/acs.iecr.0c03793 – ident: e_1_2_10_26_1 doi: 10.1016/j.sigpro.2013.12.032 |
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| SubjectTerms | Algorithms fermentation process Maximization Maximum likelihood estimates Nonlinear systems Optimization particle filter (PF) recursive EM algorithm Recursive functions unknown inputs (UIs) |
| Title | Online state and unknown inputs estimation for nonlinear systems with particle filter based recursive expectation‐maximization algorithm |
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