Gain-scheduled state estimation for discrete-time complex networks under bit-rate constraints
In this paper, the gain-scheduled state estimation issue is investigated for a kind of complex networks subject to randomly occurring nonlinearities under bit-rate constraints. An array of random variables is introduced to govern the nonlinearities whose occurring probability is a time-varying but b...
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| Veröffentlicht in: | Neurocomputing (Amsterdam) Jg. 488; S. 120 - 129 |
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Elsevier B.V
01.06.2022
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | In this paper, the gain-scheduled state estimation issue is investigated for a kind of complex networks subject to randomly occurring nonlinearities under bit-rate constraints. An array of random variables is introduced to govern the nonlinearities whose occurring probability is a time-varying but bounded value with known upper and lower bounds. A bit-rate constraint model is established and an encoding–decoding mechanism is proposed, under which an upper bound of the decoding error is acquired. The primary purpose of the issue considered in this paper is to design a gain-scheduled state estimator to obtain an estimate of the network state with an acceptable accuracy according to available output measurements. By means of the stochastic analysis and Lyapunov stability theory, a sufficient condition is provided such that the estimation error dynamics achieve the exponentially mean-square ultimate boundedness. The required estimator gain matrix is parameterized by solving a series of matrix inequalities. A numerical simulation is exploited to show the usefulness of the obtained gain-scheduled state estimator. |
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| AbstractList | In this paper, the gain-scheduled state estimation issue is investigated for a kind of complex networks subject to randomly occurring nonlinearities under bit-rate constraints. An array of random variables is introduced to govern the nonlinearities whose occurring probability is a time-varying but bounded value with known upper and lower bounds. A bit-rate constraint model is established and an encoding–decoding mechanism is proposed, under which an upper bound of the decoding error is acquired. The primary purpose of the issue considered in this paper is to design a gain-scheduled state estimator to obtain an estimate of the network state with an acceptable accuracy according to available output measurements. By means of the stochastic analysis and Lyapunov stability theory, a sufficient condition is provided such that the estimation error dynamics achieve the exponentially mean-square ultimate boundedness. The required estimator gain matrix is parameterized by solving a series of matrix inequalities. A numerical simulation is exploited to show the usefulness of the obtained gain-scheduled state estimator. |
| Author | Ding, Derui Yi, Xiaojian Zhang, Yuhan Zhao, Di Wang, Licheng |
| Author_xml | – sequence: 1 givenname: Licheng surname: Wang fullname: Wang, Licheng organization: Department of Control Science and Engineering, Shanghai Key Laboratory of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 200093, China – sequence: 2 givenname: Di surname: Zhao fullname: Zhao, Di organization: College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China – sequence: 3 givenname: Yuhan surname: Zhang fullname: Zhang, Yuhan organization: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China – sequence: 4 givenname: Derui surname: Ding fullname: Ding, Derui organization: Department of Control Science and Engineering, Shanghai Key Laboratory of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 200093, China – sequence: 5 givenname: Xiaojian surname: Yi fullname: Yi, Xiaojian organization: School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China |
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| Keywords | Randomly occurring nonlinearities Gain-scheduled state estimation Bit-rate constraints Complex networks |
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