Thematische Bibliographien / Distributed Moving Horizon Estimation / Zeitschriftenartikel
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Zeitschriftenartikel zum Thema „Distributed Moving Horizon Estimation“

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Veröffentlicht am 25. Mai 2024

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1

Venturino, Antonello, Cristina Stoica Maniu, Sylvain Bertrand, Teodoro Alamo und Eduardo F. Camacho. „Distributed moving horizon state estimation for sensor networks with low computation capabilities“. SYSTEM THEORY, CONTROL AND COMPUTING JOURNAL 1, Nr. 1 (30.06.2021): 81–87. http://dx.doi.org/10.52846/stccj.2021.1.1.14.

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This paper focuses on distributed state estimation for sensor network observing a discrete-time linear system. The provided solution is based on a Distributed Moving Horizon Estimation (DMHE) algorithm considering a pre-estimating Luenberger observer in the formulation of the local problem solved by each sensor. This leads to reduce the computation load, while preserving the accuracy of the estimation. Moreover, observability properties of local sensors are used for tuning the weights related to consensus information fusion built on a rank-based condition, in order to improve the convergence of the estimation error. Results obtained by Monte Carlo simulations are provided to compare the performance with existing approaches, in terms of accuracy of the estimations and computation time.
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2

Farina, Marcello, Giancarlo Ferrari-Trecate und Riccardo Scattolini. „Distributed moving horizon estimation for nonlinear constrained systems“. IFAC Proceedings Volumes 43, Nr. 14 (September 2010): 909–14. http://dx.doi.org/10.3182/20100901-3-it-2016.00103.

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3

Farina, Marcello, Giancarlo Ferrari-Trecate und Riccardo Scattolini. „Distributed Moving Horizon Estimation for Linear Constrained Systems“. IEEE Transactions on Automatic Control 55, Nr. 11 (November 2010): 2462–75. http://dx.doi.org/10.1109/tac.2010.2046058.

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4

Battistelli, Giorgio. „Distributed Moving-Horizon Estimation With Arrival-Cost Consensus“. IEEE Transactions on Automatic Control 64, Nr. 8 (August 2019): 3316–23. http://dx.doi.org/10.1109/tac.2018.2879598.

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5

Farina, Marcello, Giancarlo Ferrari-Trecate und Riccardo Scattolini. „Distributed moving horizon estimation for nonlinear constrained systems“. International Journal of Robust and Nonlinear Control 22, Nr. 2 (29.12.2010): 123–43. http://dx.doi.org/10.1002/rnc.1676.

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6

Wang, Shoudong, und Binqiang Xue. „Distributed Moving Horizon Fusion Estimation for Nonlinear Constrained Uncertain Systems“. Mathematics 11, Nr. 6 (20.03.2023): 1507. http://dx.doi.org/10.3390/math11061507.

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This paper studies the state estimation of a class of distributed nonlinear systems. A new robust distributed moving horizon fusion estimation (DMHFE) method is proposed to deal with the norm-bounded uncertainties and guarantee the estimation performance. Based on the given relationship between a state covariance matrix and an error covariance matrix, estimated values of the unknown parameters in the system model can be obtained. Then, a local moving horizon estimation optimization algorithm is constructed by using the measured values of sensor nodes themselves, the measured information of adjacent nodes and the prior state estimates. By solving the above nonlinear optimization problem, a local optimal state estimation is obtained. Next, based on covariance intersection (CI) fusion strategy, the local optimal state estimates sent to the fusion center are fused to derive optimal state estimates. Furthermore, the sufficient conditions for the square convergence of the fusion estimation error norm are given. Finally, a simulation example is employed to demonstrate the effectiveness of the proposed algorithm.
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7

Meynen, Sönke, Sören Hohmann und Dirk Feẞler. „Fault Detection for Distributed Uncertain Systems using Moving Horizon Estimation“. IFAC-PapersOnLine 55, Nr. 6 (2022): 234–41. http://dx.doi.org/10.1016/j.ifacol.2022.07.135.

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8

Zeng, Jing, und Jinfeng Liu. „Distributed State Estimation Based Distributed Model Predictive Control“. Mathematics 9, Nr. 12 (09.06.2021): 1327. http://dx.doi.org/10.3390/math9121327.

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In this work, we consider output-feedback distributed model predictive control (DMPC) based on distributed state estimation with bounded process disturbances and output measurement noise. Specifically, a state estimation scheme based on observer-enhanced distributed moving horizon estimation (DMHE) is considered for distributed state estimation purposes. The observer-enhanced DMHE ensures that the state estimates of the system reach a small neighborhood of the actual state values quickly and then maintain within the neighborhood. This implies that the estimation error is bounded. Based on the state estimates provided by the DMHE, a DMPC algorithm is developed based on Lyapunov techniques. In the proposed design, the DMHE and the DMPC are evaluated synchronously every sampling time. The proposed output DMPC is applied to a simulated chemical process and the simulation results show the applicability and effectiveness of the proposed distributed estimation and control approach.
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9

Zhang, Jing, und Jinfeng Liu. „Two triggered information transmission algorithms for distributed moving horizon state estimation“. Systems & Control Letters 65 (März 2014): 1–12. http://dx.doi.org/10.1016/j.sysconle.2013.12.003.

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10

Zhang, Jing, und Jinfeng Liu. „Distributed moving horizon state estimation for nonlinear systems with bounded uncertainties“. Journal of Process Control 23, Nr. 9 (Oktober 2013): 1281–95. http://dx.doi.org/10.1016/j.jprocont.2013.08.005.

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11

Zhang, Jing, und Jinfeng Liu. „Observer-enhanced distributed moving horizon state estimation subject to communication delays“. Journal of Process Control 24, Nr. 5 (Mai 2014): 672–86. http://dx.doi.org/10.1016/j.jprocont.2014.03.012.

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12

Yin, Xunyuan, und Jinfeng Liu. „Distributed moving horizon state estimation of two-time-scale nonlinear systems“. Automatica 79 (Mai 2017): 152–61. http://dx.doi.org/10.1016/j.automatica.2017.01.023.

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13

Yin, Xunyuan, Benjamin Decardi-Nelson und Jinfeng Liu. „Subsystem decomposition and distributed moving horizon estimation of wastewater treatment plants“. Chemical Engineering Research and Design 134 (Juni 2018): 405–19. http://dx.doi.org/10.1016/j.cherd.2018.04.032.

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14

Jang, Hong, Jay H. Lee, Richard D. Braatz und Kwang-Ki K. Kim. „Fast moving horizon estimation for a two-dimensional distributed parameter system“. Computers & Chemical Engineering 63 (April 2014): 159–72. http://dx.doi.org/10.1016/j.compchemeng.2013.12.005.

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15

Kim, Jinsung, Ji-Han Kang, Jinwoo Bae, Wonhyung Lee und Kwang-Ki K. Kim. „Distributed Moving Horizon Estimation via Operator Splitting for Automated Robust Power System State Estimation“. IEEE Access 9 (2021): 90428–40. http://dx.doi.org/10.1109/access.2021.3091706.

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16

Zeng, Jing, und Jinfeng Liu. „Distributed moving horizon state estimation: Simultaneously handling communication delays and data losses“. Systems & Control Letters 75 (Januar 2015): 56–68. http://dx.doi.org/10.1016/j.sysconle.2014.11.007.

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17

Lauricella, M., M. Farina, R. Schneider und R. Scattolini. „A distributed fault detection and isolation algorithm based on Moving Horizon Estimation“. IFAC-PapersOnLine 50, Nr. 1 (Juli 2017): 15259–64. http://dx.doi.org/10.1016/j.ifacol.2017.08.2406.

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18

Chen, Tengpeng, Yi Foo, K. V. Ling und Xuebing Chen. „Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems“. Sensors 17, Nr. 10 (11.10.2017): 2310. http://dx.doi.org/10.3390/s17102310.

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19

Farina, Marcello, Giancarlo Ferrari-Trecate, Carlo Romani und Riccardo Scattolini. „Moving horizon estimation for distributed nonlinear systems with application to cascade river reaches“. Journal of Process Control 21, Nr. 5 (Juni 2011): 767–74. http://dx.doi.org/10.1016/j.jprocont.2010.10.022.

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20

An, Tianrui, Xunyuan Yin, Jinfeng Liu und J. Fraser Forbes. „Coordinated distributed moving horizon state estimation for linear systems based on prediction-driven method“. Canadian Journal of Chemical Engineering 95, Nr. 10 (26.07.2017): 1953–67. http://dx.doi.org/10.1002/cjce.22917.

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21

Lauricella, M., M. Farina, R. Schneider und R. Scattolini. „Iterative distributed fault detection and isolation for linear systems based on moving horizon estimation“. International Journal of Adaptive Control and Signal Processing 34, Nr. 6 (27.10.2019): 743–56. http://dx.doi.org/10.1002/acs.3063.

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22

Li, Xiaojie, Song Bo, Yan Qin und Xunyuan Yin. „Partition-based distributed moving horizon state estimation with system disturbances and sensor noise penalties“. IFAC-PapersOnLine 56, Nr. 2 (2023): 3862–67. http://dx.doi.org/10.1016/j.ifacol.2023.10.1318.

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23

Studener, Stephan, Khaled Habaieb, Boris Lohmann und Roland Wolf. „Estimation of process parameters on a moving horizon for a class of distributed parameter systems“. Journal of Process Control 20, Nr. 1 (Januar 2010): 58–62. http://dx.doi.org/10.1016/j.jprocont.2009.10.006.

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24

Reyes Aldo Cipriano, Francisco. „Passenger Estimation using Moving Horizon Optimization* *This study was funded by the FONDECYT N1120047, “Distributed Hybrid Model Predictive Control for Mineral Processing”.“ IFAC Proceedings Volumes 45, Nr. 25 (2012): 112–17. http://dx.doi.org/10.3182/20120913-4-it-4027.00029.

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25

Farina, Marcello, Giancarlo Ferrari-Trecate und Riccardo Scattolini. „Distributed moving horizon estimation for sensor networks* *The research of M. Farina and R. Scattolini has been supported by the European 7th framework STREP project “Hierarchical and distributed model predictive control (HD-MPC)”, contract number INFSO-ICT-223854.“ IFAC Proceedings Volumes 42, Nr. 20 (September 2009): 126–31. http://dx.doi.org/10.3182/20090924-3-it-4005.00022.

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26

KARTAEV, PH S., und M. N. BESEDOVSKAYA. „IS THE PHILLIPS CURVE USEFUL FOR FORECASTING INFLATION IN RUSSIA?“ Lomonosov Economics Journal, Nr. 6_2023 (23.05.2024): 24–43. http://dx.doi.org/10.55959/msu0130-0105-6-58-6-2.

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The paper analyzes the experience of using the Phillips curve to model inflation in Russia, taking into account the specific features of domestic labor market. Based on Russian data for the period from 2000 to 2022, a wide range of inflation forecasting models has been built: both based on the Phillips curve and alternative ones. The econometric tools used are autoregression models with a moving average in the residuals taking into account seasonality (SARIMA) and their generalizations; autoregression models of distributed lags (ADL) and their generalizations; as well as other estimation methods. During the simulation, we use data on inflation, inflation expectations, production dynamics, unemployment, wages, exchange rates, money supply and other variables. The models are compared drawing on the accuracy of single-period and multi-period out-of-sample forecasts. The modelling results allows us to conclude that onedimensional models work well during the periods of stable economic dynamics, but lose in their predictive power to the “triangular” Phillips curve in crisis years. Comparison of models for forecasting inflation shows that in a stable economic situation, one-dimensional models provide a more reliable forecast. However, in the context of structural transformation faced by the Russian economy in 2022, the “triangular” models of the Phillips curve demonstrate maximum quality of the forecast. Although the acceleration of inflation in 2022 obviously reduces the accuracy of any forecast equations, however, the “triangular” model based on the lags in inflation, unemployment and the index of industrial production demonstrates the best results. This conclusion remains stable to changes in the length of the time series used for forecasting, as well as to changes in forecasting horizon.
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27

Kong, He, und Salah Sukkarieh. „Metamorphic moving horizon estimation“. Automatica 97 (November 2018): 167–71. http://dx.doi.org/10.1016/j.automatica.2018.08.018.

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28

Polóni, Tomáš, Boris Rohal-Ilkiv und Tor Arne Johansen. „Moving Horizon Estimation for Integrated Navigation Filtering**This work is supported by the ANR project entitled Hamiltonian Methods for the Control of Multidomain Distributed Parameter Systems, HAMECMOPSYS financed by the French National Research Agency. Further information is available at http://www.hamecmopsys.ens2m.fr/.“ IFAC-PapersOnLine 48, Nr. 23 (2015): 519–26. http://dx.doi.org/10.1016/j.ifacol.2015.11.331.

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29

Al-Matouq, Ali A., und Tyrone L. Vincent. „Multiple window moving horizon estimation“. Automatica 53 (März 2015): 264–74. http://dx.doi.org/10.1016/j.automatica.2014.12.002.

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30

Artzová, Petra, und Radoslav Paulen. „Moving-horizon Guaranteed Parameter Estimation“. IFAC-PapersOnLine 52, Nr. 1 (2019): 112–17. http://dx.doi.org/10.1016/j.ifacol.2019.06.046.

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31

Krämer, Stefan, und Ralf Gesthuisen. „MULTIRATE STATE ESTIMATION USING MOVING HORIZON ESTIMATION“. IFAC Proceedings Volumes 38, Nr. 1 (2005): 1–6. http://dx.doi.org/10.3182/20050703-6-cz-1902.00654.

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32

Kim, Yeonsoo, Kuan-Han Lin, David M. Thierry und Lorenz T. Biegler. „Advanced-multi-step Moving Horizon Estimation“. IFAC-PapersOnLine 54, Nr. 3 (2021): 269–74. http://dx.doi.org/10.1016/j.ifacol.2021.08.253.

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33

Barreiro, Rui F., A. Pedro Aguiar und João M. Lemos. „Moving Horizon Estimation with Decimated Observations *“. IFAC Proceedings Volumes 43, Nr. 14 (September 2010): 296–301. http://dx.doi.org/10.3182/20100901-3-it-2016.00267.

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34

Ferrari-Trecate, G., D. Mignone und M. Morari. „Moving horizon estimation for hybrid systems“. IEEE Transactions on Automatic Control 47, Nr. 10 (Oktober 2002): 1663–76. http://dx.doi.org/10.1109/tac.2002.802772.

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35

Abdollahpouri, Mohammad, Rien Quirynen, Mark Haring, Tor Arne Johansen, Gergely Takács, Moritz Diehl und Boris Rohaľ-Ilkiv. „A homotopy-based moving horizon estimation“. International Journal of Control 92, Nr. 7 (08.12.2017): 1672–81. http://dx.doi.org/10.1080/00207179.2017.1406150.

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36

Rawlings, James B., und Bhavik R. Bakshi. „Particle filtering and moving horizon estimation“. Computers & Chemical Engineering 30, Nr. 10-12 (September 2006): 1529–41. http://dx.doi.org/10.1016/j.compchemeng.2006.05.031.

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37

Sveier, Aksel, und Olav Egeland. „Pose Estimation using Dual Quaternions and Moving Horizon Estimation“. IFAC-PapersOnLine 51, Nr. 13 (2018): 186–91. http://dx.doi.org/10.1016/j.ifacol.2018.07.275.

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38

KULCSÁR, Balázs, István VARGA und József BOKOR. „CONSTRAINED SPLIT RATE ESTIMATION BY MOVING HORIZON“. IFAC Proceedings Volumes 38, Nr. 1 (2005): 78–83. http://dx.doi.org/10.3182/20050703-6-cz-1902.02036.

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39

Sun, Qing, Cheng-Chew Lim, Peng Shi und Fei Liu. „Moving horizon estimation for Markov jump systems“. Information Sciences 367-368 (November 2016): 143–58. http://dx.doi.org/10.1016/j.ins.2016.05.028.

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40

Guo, Yafeng, und Biao Huang. „Moving horizon estimation for switching nonlinear systems“. Automatica 49, Nr. 11 (November 2013): 3270–81. http://dx.doi.org/10.1016/j.automatica.2013.08.028.

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41

Kirchner, M., J. Croes, F. Cosco und W. Desmet. „Compressive sensing-moving horizon estimator for distributed loads“. Procedia Engineering 199 (2017): 447–52. http://dx.doi.org/10.1016/j.proeng.2017.09.180.

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42

Boulkroune, B., M. Darouach und M. Zasadzinski. „Optimal estimation for linear singular systems using moving horizon estimation“. IFAC Proceedings Volumes 41, Nr. 2 (2008): 14528–33. http://dx.doi.org/10.3182/20080706-5-kr-1001.02461.

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43

Brembeck, Jonathan. „Nonlinear Constrained Moving Horizon Estimation Applied to Vehicle Position Estimation“. Sensors 19, Nr. 10 (16.05.2019): 2276. http://dx.doi.org/10.3390/s19102276.

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The design of high–performance state estimators for future autonomous vehicles constitutes a challenging task, because of the rising complexity and demand for operational safety. In this application, a vehicle state observer with a focus on the estimation of the quantities position, yaw angle, velocity, and yaw rate, which are necessary for a path following control for an autonomous vehicle, is discussed. The synthesis of the vehicle’s observer model is a trade-off between modelling complexity and performance. To cope with the vehicle still stand situations, the framework provides an automatic event handling functionality. Moreover, by means of an efficient root search algorithm, map-based information on the current road boundaries can be determined. An extended moving horizon state estimation algorithm enables the incorporation of delayed low bandwidth Global Navigation Satellite System (GNSS) measurements—including out of sequence measurements—as well as the possibility to limit the vehicle position change through the knowledge of the road boundaries. Finally, different moving horizon observer configurations are assessed in a comprehensive case study, which are compared to a conventional extended Kalman filter. These rely on real-world experiment data from vehicle testdrive experiments, which show very promising results for the proposed approach.
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44

Vandersteen, Jeroen, Moritz Diehl, Conny Aerts und Jan Swevers. „Spacecraft Attitude Estimation and Sensor Calibration Using Moving Horizon Estimation“. Journal of Guidance, Control, and Dynamics 36, Nr. 3 (Mai 2013): 734–42. http://dx.doi.org/10.2514/1.58805.

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45

Sun, Liang, Jeremy D. Castagno, John D. Hedengren und Randal W. Beard. „Parameter estimation for towed cable systems using moving horizon estimation“. IEEE Transactions on Aerospace and Electronic Systems 51, Nr. 2 (April 2015): 1432–46. http://dx.doi.org/10.1109/taes.2014.130642.

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46

Tuveri, Andrea, Haakon Eng Holck, Caroline S. M. Nakama, José Matias, Johannes Jäschke, Lars Imsland und Nadav Bar. „Bioprocess Monitoring: A Moving Horizon Estimation Experimental Application“. IFAC-PapersOnLine 55, Nr. 7 (2022): 222–27. http://dx.doi.org/10.1016/j.ifacol.2022.07.448.

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47

Sánchez, Guido, Marina Murillo, Lucas Genzelis, Nahuel Deniz und Leonardo Giovanini. „Moving Horizon Estimation for GNSS-IMU sensor fusion“. Revista Tecnología y Ciencia, Nr. 37 (22.10.2020): 112–22. http://dx.doi.org/10.33414/rtyc.37.112-122.2020.

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The aim of this work is to develop a Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensor fusion system. To achieve this objective, we introduce a Moving Horizon Estimation (MHE) algorithm to estimate the position, velocity orientation and also the accelerometer and gyroscope bias of a simulated unmanned ground vehicle. The obtained results are compared with the true values of the system and with an Extended Kalman filter (EKF). The use of CasADi and Ipopt provide efficient numerical solvers that can obtain fast solutions. The quality of MHE estimated values enable us to consider MHE as a viable replacement for the popular Kalman Filter, even on real time systems.
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48

Sui, Dan, und Bernt Sigve Aadnøy. „Rate of Penetration Optimization using Moving Horizon Estimation“. Modeling, Identification and Control: A Norwegian Research Bulletin 37, Nr. 3 (2016): 149–58. http://dx.doi.org/10.4173/mic.2016.3.1.

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49

Sui, Dan, Tor Arne Johansen und Le Feng. „Linear Moving Horizon Estimation With Pre-Estimating Observer“. IEEE Transactions on Automatic Control 55, Nr. 10 (Oktober 2010): 2363–68. http://dx.doi.org/10.1109/tac.2010.2053060.

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50

Haber, Aleksandar, und Michel Verhaegen. „Moving Horizon Estimation for Large-Scale Interconnected Systems“. IEEE Transactions on Automatic Control 58, Nr. 11 (November 2013): 2834–47. http://dx.doi.org/10.1109/tac.2013.2272151.

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