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Journal articles on the topic 'Unknown Input Observer'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

Messaoud, Ramzi Ben, and Mekki Ksouri. "Local nonlinear unknown input observer." Journal of Control Theory and Applications 11, no. 3 (July 4, 2013): 517–20. http://dx.doi.org/10.1007/s11768-013-2122-y.

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2

Marx, Benoît, Dalil Ichalal, José Ragot, Didier Maquin, and Saïd Mammar. "Unknown input observer for LPV systems." Automatica 100 (February 2019): 67–74. http://dx.doi.org/10.1016/j.automatica.2018.10.054.

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3

Zhu, Fanglai, Yuhang Fu, and Thach Ngoc Dinh. "Asymptotic convergence unknown input observer design via interval observer." Automatica 147 (January 2023): 110744. http://dx.doi.org/10.1016/j.automatica.2022.110744.

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4

Chen, Maoyin, and Wang Min. "Unknown input observer based chaotic secure communication." Physics Letters A 372, no. 10 (March 2008): 1595–600. http://dx.doi.org/10.1016/j.physleta.2007.10.012.

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5

SCHREIER, G., B. GADDOUNA, and J. RAGOT. "Matrices computation of an unknown-input observer." International Journal of Systems Science 26, no. 4 (April 1995): 991–98. http://dx.doi.org/10.1080/00207729508929082.

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6

PARK, YOUNGJIN, and JEFFREY L. STEIN. "Closed-loop, state and input observer for systems with unknown inputs." International Journal of Control 48, no. 3 (September 1988): 1121–36. http://dx.doi.org/10.1080/00207178808906239.

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7

Cao, Jun-Yi, Bing-Gang Cao ., and Zhong Liu . "Driving Resistance Estimation Based on Unknown Input Observer." Journal of Applied Sciences 6, no. 4 (February 1, 2006): 888–91. http://dx.doi.org/10.3923/jas.2006.888.891.

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8

Cheviron, T., F. Plestan, and H. Chriette. "NONLINEAR UNKNOWN INPUT OBSERVER IN A STOCHASTIC FRAMEWORK." IFAC Proceedings Volumes 40, no. 12 (2007): 651–56. http://dx.doi.org/10.3182/20070822-3-za-2920.00107.

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9

Youssfi, Naoufal El, Mohammed Oudghiri, and Rachid El Bachtiri. "Vehicle lateral dynamics estimation using unknown input observer." Procedia Computer Science 148 (2019): 502–11. http://dx.doi.org/10.1016/j.procs.2019.01.063.

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10

Zheng, Gang, Francisco Javier Bejarano, Wilfrid Perruquetti, and Jean-Pierre Richard. "Unknown input observer for linear time-delay systems." Automatica 61 (November 2015): 35–43. http://dx.doi.org/10.1016/j.automatica.2015.07.029.

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11

Abdelmalek, Samir, Linda Barazane, and Abdelkader Larabi. "Unknown Input Observer for a Doubly Fed Induction Generator Subject to Disturbances." International Journal of Power Electronics and Drive Systems (IJPEDS) 6, no. 4 (December 1, 2015): 781. http://dx.doi.org/10.11591/ijpeds.v6.i4.pp781-787.

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This paper deals with the problem design of an unknown input observer (UIO) for a Doubly Fed Induction Generator (DFIG) subject to disturbances. These disturbances can be considered as unknown inputs (UI). The state space model of the DFIG is obtained from the voltage equations of the stator and rotor. Then, this latter is used for the design of an unknown input observer (UIO) in order to estimate both the state and the unknown inputs of the DFIG. Furthermore, the UIO gains are computed by solving a set of linear matrix inequalities (LMIs). Simulations results are given to show the performance and the effectiveness of the proposed method.

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12

SAGARA, Setsuo, Takami MATSUO, and Kazushi NAKANO. "Design of Unknown Input Observer by Spectral Decomposition Approach." Transactions of the Society of Instrument and Control Engineers 21, no. 3 (1985): 213–19. http://dx.doi.org/10.9746/sicetr1965.21.213.

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13

HU, Zhi-Kun, Yan SUN, Bin JIANG, Jing HE, and Chang-Fan ZHANG. "An Optimal Unknown Input Observer Based Fault Diagnosis Method." Acta Automatica Sinica 39, no. 8 (June 12, 2014): 1225–30. http://dx.doi.org/10.3724/sp.j.1004.2013.01225.

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14

Meslem, Nacim, Ahmad Hably, Tarek Raïssi, and Zhenhua Wang. "Reliable Unknown Input Observer for Continuous-Time Linear Systems." IFAC-PapersOnLine 55, no. 6 (2022): 248–53. http://dx.doi.org/10.1016/j.ifacol.2022.07.137.

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15

Gao, Zhiwei, and Steven X. Ding. "PMID OBSERVER DESIGN FOR UNKNOWN INPUT GENERALIZED DYNAMICAL SYSTEMS." IFAC Proceedings Volumes 38, no. 1 (2005): 1251–56. http://dx.doi.org/10.3182/20050703-6-cz-1902.00209.

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16

Krzemiński, Stefan, and Tadeusz Kaczorek. "REDUCED-ORDER UNKNOWN-INPUT PERFECT OBSERVER FOR STANDARD SYSTEMS." IFAC Proceedings Volumes 38, no. 1 (2005): 1257–62. http://dx.doi.org/10.3182/20050703-6-cz-1902.00210.

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17

Kiyak, Emre, and Ahmet Ermeydan. "Fault diagnosis for a UAV using unknown input observer." International Journal of Sustainable Aviation 2, no. 4 (2016): 309. http://dx.doi.org/10.1504/ijsa.2016.082198.

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18

Ermeydan, Ahmet, and Emre Kiyak. "Fault diagnosis for a UAV using unknown input observer." International Journal of Sustainable Aviation 2, no. 4 (2016): 309. http://dx.doi.org/10.1504/ijsa.2016.10002991.

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19

Rafaralahy, Hugues, Michel Zasadzinski, Chokri Mechmeche, and Mohamed Darouach. "Unknown Input Observer for Bilinear Systems with Gain Optimisation." IFAC Proceedings Volumes 29, no. 1 (June 1996): 4368–73. http://dx.doi.org/10.1016/s1474-6670(17)58368-8.

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20

Chen, Min-Shin, and Chi-Che Chen. "Unknown input observer for linear non-minimum phase systems." Journal of the Franklin Institute 347, no. 2 (March 2010): 577–88. http://dx.doi.org/10.1016/j.jfranklin.2009.12.002.

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21

Du, Xiao-Kun, Hui Zhao, and Xiao-Heng Chang. "Unknown input observer design for fuzzy systems with uncertainties." Applied Mathematics and Computation 266 (September 2015): 108–18. http://dx.doi.org/10.1016/j.amc.2015.05.046.

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22

Saoudi, D., M. Chadli, C. Mechmeche, and N. Benhadj Braiek. "Unknown Input Observer Design for Fuzzy Bilinear System: An LMI Approach." Mathematical Problems in Engineering 2012 (2012): 1–21. http://dx.doi.org/10.1155/2012/794581.

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A new method to design a fuzzy bilinear observer (FBO) with unknown inputs is developed for a class of nonlinear systems. The nonlinear system is modeled as a fuzzy bilinear model (FBM). This kind of T-S fuzzy model is especially suitable for a nonlinear system with a bilinear term. The proposed fuzzy bilinear observer subject to unknown inputs is developed to ensure the asymptotic convergence of the error dynamic using the Lyapunov method. The proposed design conditions are given in linear matrix inequality (LMI) formulation. The paper studies also the problem of fault detection and isolation. An unknown input fuzzy bilinear fault diagnosis observer design is proposed. This work is given for both continuous and discrete cases of fuzzy bilinear models. Illustrative examples are chosen to provide the effectiveness of the given methodology.

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23

Zhan, Tao, and Shuping Ma. "Reduced-order observer design with unknown input for fractional order descriptor nonlinear systems." Transactions of the Institute of Measurement and Control 41, no. 13 (April 8, 2019): 3705–13. http://dx.doi.org/10.1177/0142331219834990.

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This paper investigates the observer design issues for the quadratic inner-bounded nonlinear descriptor fractional order systems. Considering the disturbances or inaccessible partial inputs, the order reduction of observer with unknown input is firstly implemented for effectively estimating the system state vectors. Then, for the purpose of the system conservatism reduction, the matrix [Formula: see text] and the matrix generalized inverse technique are applied to design the reduced-order observer, which can deal with unknown input with less restrictions of coefficient matrices. By using fractional order Lyapunov direct method, sufficient conditions can be obtained to ensure the existence of the designed observer. Finally, a fractional order electrical circuit is applied to demonstrate the applicability of the proposed approach.

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24

Zhang, Jiancheng, and Fanglai Zhu. "On the observer matching condition and unknown input observer design based on the system left-invertibility concept." Transactions of the Institute of Measurement and Control 40, no. 9 (July 26, 2017): 2887–900. http://dx.doi.org/10.1177/0142331217711494.

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This paper considers the problems of unknown input observer (UIO) designs when the so-called observer matching condition (OMC) is not satisfied. Firstly, the system left-invertibility (SLI) concept is investigated in detail and some new criteria are given. Secondly, based on the SLI concept, an augmented output vector formed partly by the original measurable output and partly by the auxiliary output is obtained. By this constructed output vector, the new system is allowed to be transformed into an equivalent linear dynamic system which contains no unknown input, and thus a simple Luenberger observer can be constructed to simultaneously estimate the original states and unknown inputs. Compared with the existing results, the present UIO design can be taken under the single strong detectability condition and the OMC is not needed any more. Finally, the simulations of a single-link flexible joint robotic model and a linearized vertical take-off and landing helicopter model are given to show the effectiveness of our method and its superiority over existing ones.

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25

KIMURA, Takuro, Akira OHSUMI, and Michio KONO. "Observer-based Identification of Unknown Exogenous Input via Pseudomeasurement Approach." SICE Journal of Control, Measurement, and System Integration 2, no. 3 (2009): 184–91. http://dx.doi.org/10.9746/jcmsi.2.184.

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26

Yang, D., E. Tarasov, C. Sueur, and B. Ould Bouamama. "Unknown Input Observer for control design: a bond graph approach." IFAC Proceedings Volumes 46, no. 2 (2013): 611–16. http://dx.doi.org/10.3182/20130204-3-fr-2033.00051.

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27

TAHRAOUI, Souad, Abdelmadjid MEGHABBAR, and Djamila BOUBEKEUR. "Unknown input observer based on LMI for robust generation residuals." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 25 (2017): 95–107. http://dx.doi.org/10.3906/elk-1406-1.

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28

Efimov, Denis, Ali Zolghadri, and Tarek Raïssi. "HYBRID UNKNOWN INPUT OBSERVER FOR ACTUATOR FAULT DETECTION AND COMPENSATION." IFAC Proceedings Volumes 44, no. 1 (January 2011): 4356–61. http://dx.doi.org/10.3182/20110828-6-it-1002.02164.

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29

Ichalal, Dalil, and Said Mammar. "Asymptotic Unknown Input Decoupling Observer for Discrete-Time LTI Systems." IEEE Control Systems Letters 4, no. 2 (April 2020): 361–66. http://dx.doi.org/10.1109/lcsys.2019.2929351.

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30

Akhenak, A., M. Chadli, J. Ragot, and D. Maquin. "UNKNOWN INPUT MULTIPLE OBSERVER BASED-APPROACH - APPLICATION TO SECURE COMMUNICATIONS." IFAC Proceedings Volumes 39, no. 8 (2006): 172–77. http://dx.doi.org/10.3182/20060628-3-fr-3903.00032.

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31

Koenig, D., and S. Mammar. "Design of proportional-integral observer for unknown input descriptor systems." IEEE Transactions on Automatic Control 47, no. 12 (December 2002): 2057–62. http://dx.doi.org/10.1109/tac.2002.805675.

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32

Liu, H. Y., and Z. S. Duan. "Unknown input observer design for systems with monotone non-linearities." IET Control Theory & Applications 6, no. 12 (August 16, 2012): 1941–47. http://dx.doi.org/10.1049/iet-cta.2011.0611.

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33

Zarei, Jafar, and Ehsan Shokri. "Robust sensor fault detection based on nonlinear unknown input observer." Measurement 48 (February 2014): 355–67. http://dx.doi.org/10.1016/j.measurement.2013.11.015.

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34

Weitian Chen and Mehrdad Saif. "Fuzzy Nonlinear Unknown Input Observer Design with Fault Diagnosis Applications." Journal of Vibration and Control 16, no. 3 (March 2010): 377–401. http://dx.doi.org/10.1177/1077546309106525.

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35

Zarei, Jafar, and Javad Poshtan. "Design of Nonlinear Unknown Input Observer for Process Fault Detection." Industrial & Engineering Chemistry Research 49, no. 22 (November 17, 2010): 11443–52. http://dx.doi.org/10.1021/ie100477m.

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36

Dabladji, Mohammed El-Habib, Dalil Ichalal, Hichem Arioui, and Saïd Mammar. "Unknown-input observer design for motorcycle lateral dynamics: TS approach." Control Engineering Practice 54 (September 2016): 12–26. http://dx.doi.org/10.1016/j.conengprac.2016.05.005.

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37

Ellero, N., D. Gucik-Derigny, and D. Henry. "Unknown input interval observer with H ∞ and D-stability performance." IFAC-PapersOnLine 50, no. 1 (July 2017): 6251–58. http://dx.doi.org/10.1016/j.ifacol.2017.08.850.

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38

Zhang, Wei, Housheng Su, Fanglai Zhu, and Ghassan M. Azar. "Unknown input observer design for one-sided Lipschitz nonlinear systems." Nonlinear Dynamics 79, no. 2 (October 26, 2014): 1469–79. http://dx.doi.org/10.1007/s11071-014-1754-x.

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39

Gucik-Derigny, D., T. Raïssi, and A. Zolghadri. "A note on interval observer design for unknown input estimation." International Journal of Control 89, no. 1 (June 25, 2015): 25–37. http://dx.doi.org/10.1080/00207179.2015.1057230.

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40

Guo, Shenghui, Fanglai Zhu, and Liyun Xu. "Unknown input observer design for Takagi-Sugeno fuzzy stochastic system." International Journal of Control, Automation and Systems 13, no. 4 (March 28, 2015): 1003–9. http://dx.doi.org/10.1007/s12555-014-0190-5.

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41

Hayar, M., M. Zasadzinski, and M. Darouach. "Quadratic Stabilization of Uncertain Linear Systems by Unknown Input Observer." IFAC Proceedings Volumes 28, no. 8 (July 1995): 221–23. http://dx.doi.org/10.1016/s1474-6670(17)45465-6.

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42

Ye, Jing Jie, Yu Wan Cen, Xiao Hua Ye, Pei Min Xu, and Zi Wei Pan. "An Observer and Observer Based Modeling Lateral Dynamics For Moving Steel Strip." Advanced Materials Research 422 (December 2011): 286–95. http://dx.doi.org/10.4028/www.scientific.net/amr.422.286.

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The steel strip processing systems rely on accurate lateral positioning to achieve high processing speeds and improved product quality. In real steel strip process systems are not just nonlinear and time-varying but also highly uncertain. This research focuses on the disturbance estimation approach to modeling unknown dynamics of physical plant. The Euler’s beam theory with boundary condition is considered to derive the equation of the moving steel strip. To consider unknown dynamics in the real steel strip, a nonlinear state space equation is employed to describe unknown dynamics of the moving steel strip. The nonlinear part of this system is proposed as an unknown disturbance input to this system. The neural-based observer (NNO) is designed to estimate nonlinear unknown dynamics. The online weight-updating mechanism is a modified version of the backpropagation algorithm with a simple structure together with an error-modification term added to guarantee the robustness of the observer. The measure of sensor and estimation of observer pair of input and output is used to train the weight of NN. The simulation and measure result shown that the NNO can track the un-modeling dynamic of nonlinear systems.

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43

Bouassem, Karim, Abdellatif El Assoudi, Jalal Soulami, and El Hassane El Yaagoubi. "Unknown Input Observer Design for a Class of Linear Descriptor Systems." E3S Web of Conferences 229 (2021): 01019. http://dx.doi.org/10.1051/e3sconf/202122901019.

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This paper addresses the problem of unknown inputs observer (UIO) design for a class of linear descriptor systems. The unknown inputs affect both state and output of the system. The basic idea of the proposed approach is based on the separation between dynamic and static relations in the descriptor model. Firstly, the method used to separate the differential part from the algebraic part is developed. Secondly, an observer design permitting the simultaneous estimation of the system state and the unknown inputs is proposed. The developed approach for the observer design is based on the synthesis of an augmented model which regroups the differential variables and unknown inputs. The exponential stability of the estimation error is studied using the Lyapunov theory and the stability condition is given in term of linear matrix inequality (LMI). Finally, to illustrate the efficiency of the proposed methodology, a heat exchanger pilot model is considered.

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44

Youssef, T., M. Chadli, H. R. Karimi, and M. Zelmat. "Chaos Synchronization Based on Unknown Input Proportional Multiple-Integral Fuzzy Observer." Abstract and Applied Analysis 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/670878.

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This paper presents an unknown input Proportional Multiple-Integral Observer (PIO) for synchronization of chaotic systems based on Takagi-Sugeno (TS) fuzzy chaotic models subject to unmeasurable decision variables and unknown input. In a secure communication configuration, this unknown input is regarded as a message encoded in the chaotic system and recovered by the proposed PIO. Both states and outputs of the fuzzy chaotic models are subject to polynomial unknown input withkth derivative zero. Using Lyapunov stability theory, sufficient design conditions for synchronization are proposed. The PIO gains matrices are obtained by resolving linear matrix inequalities (LMIs) constraints. Simulation results show through two TS fuzzy chaotic models the validity of the proposed method.

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45

Messaoud, Ramzi Ben. "Nonlinear unknown input observer using mean value theorem and genetic algorithm." International Journal of Modeling, Simulation, and Scientific Computing 10, no. 02 (April 2019): 1950001. http://dx.doi.org/10.1142/s1793962319500016.

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In this note, we consider a new unknown input observer design for nonlinear systems. The main idea consists in determining the estimation error and mean value theorem parameters ([Formula: see text]) to introduce them into proposed observer structure. This process is designed on the basis of mean value theorem and genetic algorithm. The stability study relies on the use of a classical quadratic Lyapunov function. The observer’s gains are determined systematically. For the validation of theoretical development proposed in this paper, we consider two practical realizations that deals with the secure communication problem.

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46

Frank, P. M., and B. Koppen. "Review of Optimal Solutions to the Robustness Problem in Observer-Based Fault Detection." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 207, no. 2 (May 1993): 105–12. http://dx.doi.org/10.1243/pime_proc_1993_207_324_02.

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The paper presents a unified approach and general solution of the robustness problem in fault detection and isolation concepts. The ultimate objective is the design of an unknown input fault-detection observer providing a perfect decoupling between unknown inputs and faults. If this is not possible because certain prerequisites are not fulfilled, two optimal compromises in the time domain and in the frequency domain are described. The basic definitions concerning robustness and unknown input fault detectability are given, and the design techniques for the proposed approaches are outlined. The cross-connections to other methods are discussed and a practical example is given.

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47

Schaum, Alexander. "An unknown input observer for a class of diffusion-convection-reaction systems." at - Automatisierungstechnik 66, no. 7 (July 26, 2018): 548–57. http://dx.doi.org/10.1515/auto-2018-0053.

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Abstract In this paper the problem of observer design for a class of 1D scalar semi-linear parabolic distributed parameter systems with unknown boundary input is addressed. An unknown input observer is proposed in which the measurement information is imposed directly at the measurement point in form of an algebraic constraint, similar to the reduced order observer in a finite-dimensional set-up. Sufficient conditions for strong state detectability of the system and exponential convergence of the observer are drawn in terms of measurement locations and system parameters. The approach is illustrated with two case studies: (i) an unstable heat equation, and (ii) a diffusion-convection-reaction system with multiple steady-state attractor profiles.

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48

Park, Y., and J. L. Stein. "Steady-State Optimal State and Input Observer for Discrete Stochastic Systems." Journal of Dynamic Systems, Measurement, and Control 111, no. 2 (June 1, 1989): 121–27. http://dx.doi.org/10.1115/1.3153026.

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Model-based machine diagnostics techniques require the modeled states and machine inputs to be measured. Because measurement of all the states and inputs is not always possible or practical, a simultaneous state and input observer is required. Previous work has developed this type of acausal observer and shown it is susceptible to noise. This paper develops a steady-state optimal observer that minimizes the trace of the steady-state error covariance of the state and input estimates for discrete, linear, time-invariant, stochastic systems with unknown inputs. In addition, a method to distinguish the best measurement set among the available measurement sets is developed. Results from numerical simulations show that the optimal observer can greatly improve estimation results in some cases.

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49

Tsai, Jason Sheng-Hong, Yang-Fang Chen, Te-Jen Su, Chia-Yuan Chang, Shu-Mei Guo, Leang-San Shieh, and Jose I. Canelon. "A Modified Functional Observer-Based EID Estimator for Unknown Continuous-Time Singular Systems." Applied Sciences 10, no. 7 (March 28, 2020): 2316. http://dx.doi.org/10.3390/app10072316.

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This paper presents the design of a linear quadratic analog tracker (LQAT) based on the observer–Kalman-filter identification (OKID) method and the design of a modified functional observer-based equivalent input disturbance (EID) estimator for unknown square–non-square singular analog systems with unknown input and output disturbances. First, an equivalent mathematical model of the singular analog system is presented to simulate the time response of continuous-time linear singular analog systems to arbitrary inputs via the model conversion method. Then, for the unknown singular analog system, it constructs a linear quadratic analog tracker with state feedback and feed-forward gains based on the off-line OKID method. Furthermore, it extends the design methodology of the EID estimator for strictly proper regular systems with unknown matched–mismatched input and output disturbances to proper regular systems. It is important to mention that the newly developed modified functional observer for proper systems is used to estimate the unknown EID of singular analog systems and that the constraints on the dimensions of unknown disturbances can be eliminated by using the newly proposed EID estimation method. The contributions of this paper can be listed as follows: (1) based on both the OKID method and the discrete-to-continuous model conversion, the simulation of the time responses of the continuous-time linear singular models (which are not feasible using existing MATLAB toolboxes) become feasible; (2) for effective control of the unknown singular analog system, an off-line OKID method is proposed to design an LQAT with state feedback and feed-forward gains; and (3) based on the newly developed modified functional observer for the reduced-order proper regular system, the original EID estimator in the literature is newly extended to estimate the EID from the unknown strictly proper singular analog system, without the original dimensional constraints of the disturbances. It is important to mention that the disturbances of interest can be unknown matched–mismatched input and output disturbances.

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50

Li, Shanzhi, Haoping Wang, Abdel Aitouche, Yang Tian, and Nicolai Christov. "Active fault tolerance control of a wind turbine system using an unknown input observer with an actuator fault." International Journal of Applied Mathematics and Computer Science 28, no. 1 (March 1, 2018): 69–81. http://dx.doi.org/10.2478/amcs-2018-0005.

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AbstractThis paper proposes a fault tolerant control scheme based on an unknown input observer for a wind turbine system subject to an actuator fault and disturbance. Firstly, an unknown input observer for state estimation and fault detection using a linear parameter varying model is developed. By solving linear matrix inequalities (LMIs) and linear matrix equalities (LMEs), the gains of the unknown input observer are obtained. The convergence of the unknown input observer is also analysed with Lyapunov theory. Secondly, using fault estimation, an active fault tolerant controller is applied to a wind turbine system. Finally, a simulation of a wind turbine benchmark with an actuator fault is tested for the proposed method. The simulation results indicate that the proposed FTC scheme is efficient.

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