Event-Triggering State and Fault Estimation for a Class of Nonlinear Systems Subject to Sensor Saturations
This paper is concerned with the state and fault estimation issue for nonlinear systems with sensor saturations and fault signals. For the sake of avoiding the communication burden, an event-triggering protocol is utilized to govern the transmission frequency of the measurements from the sensor to i...
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| Published in | Sensors (Basel, Switzerland) Vol. 21; no. 4; p. 1242 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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10.02.2021
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| Abstract | This paper is concerned with the state and fault estimation issue for nonlinear systems with sensor saturations and fault signals. For the sake of avoiding the communication burden, an event-triggering protocol is utilized to govern the transmission frequency of the measurements from the sensor to its corresponding recursive estimator. Under the event-triggering mechanism (ETM), the current transmission is released only when the relative error of measurements is bigger than a prescribed threshold. The objective of this paper is to design an event-triggering recursive state and fault estimator such that the estimation error covariances for the state and fault are both guaranteed with upper bounds and subsequently derive the gain matrices minimizing such upper bounds, relying on the solutions to a set of difference equations. Finally, two experimental examples are given to validate the effectiveness of the designed algorithm. |
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| AbstractList | This paper is concerned with the state and fault estimation issue for nonlinear systems with sensor saturations and fault signals. For the sake of avoiding the communication burden, an event-triggering protocol is utilized to govern the transmission frequency of the measurements from the sensor to its corresponding recursive estimator. Under the event-triggering mechanism (ETM), the current transmission is released only when the relative error of measurements is bigger than a prescribed threshold. The objective of this paper is to design an event-triggering recursive state and fault estimator such that the estimation error covariances for the state and fault are both guaranteed with upper bounds and subsequently derive the gain matrices minimizing such upper bounds, relying on the solutions to a set of difference equations. Finally, two experimental examples are given to validate the effectiveness of the designed algorithm. This paper is concerned with the state and fault estimation issue for nonlinear systems with sensor saturations and fault signals. For the sake of avoiding the communication burden, an event-triggering protocol is utilized to govern the transmission frequency of the measurements from the sensor to its corresponding recursive estimator. Under the event-triggering mechanism (ETM), the current transmission is released only when the relative error of measurements is bigger than a prescribed threshold. The objective of this paper is to design an event-triggering recursive state and fault estimator such that the estimation error covariances for the state and fault are both guaranteed with upper bounds and subsequently derive the gain matrices minimizing such upper bounds, relying on the solutions to a set of difference equations. Finally, two experimental examples are given to validate the effectiveness of the designed algorithm.This paper is concerned with the state and fault estimation issue for nonlinear systems with sensor saturations and fault signals. For the sake of avoiding the communication burden, an event-triggering protocol is utilized to govern the transmission frequency of the measurements from the sensor to its corresponding recursive estimator. Under the event-triggering mechanism (ETM), the current transmission is released only when the relative error of measurements is bigger than a prescribed threshold. The objective of this paper is to design an event-triggering recursive state and fault estimator such that the estimation error covariances for the state and fault are both guaranteed with upper bounds and subsequently derive the gain matrices minimizing such upper bounds, relying on the solutions to a set of difference equations. Finally, two experimental examples are given to validate the effectiveness of the designed algorithm. |
| Author | Huang, Cong Shen, Bo Zou, Lei Shen, Yuxuan |
| AuthorAffiliation | 5 Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, China 1 College of Information Science and Technology, Donghua University, Shanghai 201620, China; c.huang@mail.dhu.edu.cn 2 Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai 201620, China 3 College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China; zouleicup@gmail.com 4 Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing 163318, China; shenyuxuan5973@163.com |
| AuthorAffiliation_xml | – name: 4 Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing 163318, China; shenyuxuan5973@163.com – name: 5 Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, China – name: 2 Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai 201620, China – name: 1 College of Information Science and Technology, Donghua University, Shanghai 201620, China; c.huang@mail.dhu.edu.cn – name: 3 College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China; zouleicup@gmail.com |
| Author_xml | – sequence: 1 givenname: Cong surname: Huang fullname: Huang, Cong – sequence: 2 givenname: Bo surname: Shen fullname: Shen, Bo – sequence: 3 givenname: Lei surname: Zou fullname: Zou, Lei – sequence: 4 givenname: Yuxuan surname: Shen fullname: Shen, Yuxuan |
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| Cites_doi | 10.1016/j.automatica.2018.03.005 10.1109/TSMC.2017.2723623 10.1109/78.485915 10.3390/s18030731 10.1016/j.jfranklin.2019.01.044 10.1109/TMECH.2018.2806350 10.1049/iet-cta.2015.0508 10.1109/TAC.2016.2614486 10.1016/j.automatica.2018.07.027 10.1002/rnc.2960 10.1016/j.automatica.2015.11.008 10.1109/TCYB.2014.2347697 10.3390/s18020321 10.1109/ChiCC.2014.6895885 10.1002/rnc.3221 10.1016/j.automatica.2012.01.008 10.1109/TCYB.2019.2932460 10.1109/TAES.2002.1039404 10.1080/00207721.2020.1754960 10.1016/j.sigpro.2016.12.004 10.1109/TFUZZ.2011.2168961 10.1007/s00521-016-2271-2 10.1115/1.3662552 10.1002/asjc.595 10.1016/j.automatica.2018.11.010 10.1016/j.jfranklin.2019.12.008 10.1016/j.neucom.2016.06.016 10.1109/TCSI.2019.2949014 10.1109/MED.2018.8442908 10.1109/TCYB.2018.2802044 10.1016/j.jfranklin.2019.08.029 10.1080/00207721.2020.1755476 |
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| Keywords | event-triggering mechanism (ETM) state and fault estimation nonlinear system sensor saturations recursive estimator |
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| Title | Event-Triggering State and Fault Estimation for a Class of Nonlinear Systems Subject to Sensor Saturations |
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