Recursive state estimation for two-dimensional shift-varying systems with random parameter perturbation and dynamical bias
In this paper, the state estimation problem is investigated for a class of two-dimensional (2-D) stochastic systems with shift-varying parameters. The stochasticity with the underlying system comes from three sources, namely, random parameter perturbations, dynamical biases and additive white noises...
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| Published in | Automatica (Oxford) Vol. 112; p. 108658 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.02.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0005-1098 1873-2836 |
| DOI | 10.1016/j.automatica.2019.108658 |
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| Abstract | In this paper, the state estimation problem is investigated for a class of two-dimensional (2-D) stochastic systems with shift-varying parameters. The stochasticity with the underlying system comes from three sources, namely, random parameter perturbations, dynamical biases and additive white noises. The measurement output is subject to the uniform quantization as a result of communication constraints on the sensors. The purpose of the addressed problem is to recursively estimate the system states with prescribed estimation performance in spite of the stochastic disturbances and the uniform quantizations. An augmented model is first constructed to combine the information about the state and the bias, and the dynamical evolution is subsequently discussed. Furthermore, through stochastic analysis and mathematical induction, the estimator design algorithm is developed to acquire certain upper bound on the estimation error variance and such an upper bound is then minimized in the matrix-trace sense. Moreover, the boundedness issue of the estimation errors is investigated via matrix inversion lemma and inductive analysis technique. Finally, the effectiveness of the proposed state estimation algorithm is validated on a typical 2-D system modeled by the well-known Darboux equation with extensive application potentials in industrial processes. |
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| AbstractList | In this paper, the state estimation problem is investigated for a class of two-dimensional (2-D) stochastic systems with shift-varying parameters. The stochasticity with the underlying system comes from three sources, namely, random parameter perturbations, dynamical biases and additive white noises. The measurement output is subject to the uniform quantization as a result of communication constraints on the sensors. The purpose of the addressed problem is to recursively estimate the system states with prescribed estimation performance in spite of the stochastic disturbances and the uniform quantizations. An augmented model is first constructed to combine the information about the state and the bias, and the dynamical evolution is subsequently discussed. Furthermore, through stochastic analysis and mathematical induction, the estimator design algorithm is developed to acquire certain upper bound on the estimation error variance and such an upper bound is then minimized in the matrix-trace sense. Moreover, the boundedness issue of the estimation errors is investigated via matrix inversion lemma and inductive analysis technique. Finally, the effectiveness of the proposed state estimation algorithm is validated on a typical 2-D system modeled by the well-known Darboux equation with extensive application potentials in industrial processes. |
| ArticleNumber | 108658 |
| Author | Wang, Fan Liang, Jinling Wang, Zidong Liu, Xiaohui |
| Author_xml | – sequence: 1 givenname: Fan surname: Wang fullname: Wang, Fan email: fanwang@seu.edu.cn organization: School of Automation, Southeast University, Nanjing 210096, China – sequence: 2 givenname: Zidong surname: Wang fullname: Wang, Zidong email: Zidong.Wang@brunel.ac.uk organization: Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom – sequence: 3 givenname: Jinling surname: Liang fullname: Liang, Jinling email: jinlliang@seu.edu.cn organization: School of Mathematics, Southeast University, Nanjing 210096, China – sequence: 4 givenname: Xiaohui surname: Liu fullname: Liu, Xiaohui email: Xiaohui.Liu@brunel.ac.uk organization: Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom |
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| Keywords | Two-dimensional systems Recursive state estimation Random parameter perturbations Signal quantization Stochastic bias |
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| SubjectTerms | Random parameter perturbations Recursive state estimation Signal quantization Stochastic bias Two-dimensional systems |
| Title | Recursive state estimation for two-dimensional shift-varying systems with random parameter perturbation and dynamical bias |
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