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Variance‐constrained resilient H filtering for mobile robot localization under dynamic event‐triggered communication mechanism

Published: 01 October 2021 Publication History

Abstract

This paper is concerned with the mobile robot localization problem subject to filter gain uncertainty under dynamic event‐triggered communication mechanism, and meanwhile, the H filtering performance and the error variance constraint are guaranteed. For saving the sensor energy, a dynamic event‐triggered communication mechanism is introduced to manage the transmission of the measurement data from the sensor to the filter. To characterize the possible fluctuations of the desired filter gain, a resilient filter is constructed for the mobile robot localization. The aim of this paper is to find a solution to the mobile robot localization problem by designing a nonlinear resilient filter such that the filtering error dynamics satisfies both the H performance requirement and the error variance constraint over a finite time horizon simultaneously. By resorting to the Lyapunov theory and the stochastic analysis technique, the sufficient conditions are established to guarantee that the error dynamic system satisfies both the H performance requirement and the error variance constraint. Then, a recursive linear matrix inequality (RLMI) approach is employed to design the desired filter. Based on the proposed filter design scheme, the corresponding localization algorithm is presented. Finally, an experiment is conducted in the simulation environment to verify the effectiveness of the proposed localization algorithm.

References

[1]
G. Calafiore, Reliable localization using set‐valued nonlinear filters, IEEE Trans. Syst. Man, Cybern.‐Part A: Syst. Humans 35 (2005), no. 2, 189–197.
[2]
R. P. Guan, B Ristic, L Wang and R Evans. Monte Carlo localisation of a mobile robot using a doppler–azimuth radar, Automatica 97 (2018), 161–166.
[3]
R. Havangi, Mobile robot localization based on PSO estimator, Asian J. Control 21 (2019), 2167–2178.
[4]
Y. Lu and B. Shen, Mobile robot localization under stochastic communication protocol, Kybernetika 56 (2020), no. 1, 152–169.
[5]
Q. Sun, Y. Tian, and M. Diao, Cooperative localization algorithm based on hybrid topology architecture for multiple mobile robot system, IEEE Int. Things J. 5 (2018), no. 6, 4753–4763.
[6]
Z. Wang, H. Dong, S. Shen and H. Gao. Finite‐horizon H filtering with missing measurements and quantization effects, IEEE Trans. Autom. Control 58 (2013), no. 7, 1707–1718.
[7]
Y. Zhuang, Q. Wang, M. Shi, P. Cao, L. Qi and J. Yang. Low‐power centimeter‐level localization for indoor mobile robots based on ensemble Kalman smoother using received signal strength, IEEE Int. Things J. 6 (2019), no. 4, 6513–6522.
[8]
F. Wang, Z. Wang, J. Liang and X. Liu. Resilient filtering for linear time‐varying repetitive processes under uniform quantizations and round‐robin protocols, IEEE Trans. Circ. Syst. I: Reg. Papers 65 (2018), no. 9, 2992–3004.
[9]
W. Chen, D. Ding, H. Dong and G. Wei. Distributed resilient filtering for power systems subject to denial‐of‐service attacks, IEEE Trans. Syst. Man, Cybern.: Syst. 49 (2019), no. 8, 1688–1697.
[10]
Q. Liu, Z. Wang, X. He, G. Ghinea and F. E. Alsaadi. A resilient approach to distributed filter design for time‐varying systems under stochastic nonlinearities and sensor degradation, IEEE Trans. Signal Process. 65 (2016), no. 5, 1300–1309.
[11]
R. Sakthivel, H. R. Karimi, M. Joby and S. Santra. Resilient sampled‐data control for Markovian jump systems with an adaptive fault‐tolerant mechanism, IEEE Trans. Circ. Syst. II: Express Briefs 64 (2017), no. 11, 1312–1316.
[12]
W. Li, Y. Jia, and J. Du, Resilient filtering for nonlinear complex networks with multiplicative noise, IEEE Trans. Autom. Control 64 (2018), no. 6, 2522–2528.
[13]
J. Tao, Z‐G. Wu, H. Su, Y. Wu and D. Zhang. Asynchronous and resilient filtering for Markovian jump neural networks subject to extended dissipativity, IEEE Trans. Cybern. 49 (2018), no. 7, 2504–2513.
[14]
L. Zhang, Y. Zhu, P. Shi and Y. Zhao. Resilient asynchronous H filtering for markov jump neural networks with unideal measurements and multiplicative noises, IEEE Trans. Cybern. 45 (2015), no. 12, 2840–2852.
[15]
W. Fu, J. Qin, Y. Shi, W. X. Zheng and Y. Kang. Resilient consensus of discrete‐time complex cyber‐physical networks under deception attacks, IEEE Trans. Indust. Inform. 16 (2019), no. 7, 4868–4877.
[16]
Q. Sun, K. Zhang, and Y. Shi, Resilient model predictive control of cyber–physical systems under DoS attacks, IEEE Trans. Indust. Inform. 16 (2019), no. 7, 4920–4927.
[17]
L. Ding et al., An overview of recent advances in event‐triggered consensus of multiagent systems, IEEE Trans. Cybern. 48 (2017), no. 4, 1110–1123.
[18]
B. Jiang H. R. Karimi, Y. Kao and C. Gao. Takagi‐Sugeno model based event‐triggered fuzzy sliding‐mode control of networked control systems with semi‐Markovian switchings, IEEE Trans. Fuzzy Syst. 28 (2019), no. 4, 673–683.
[19]
J. Liu, T. Yin, D. Yue, H. R. Karimi and J. Cao. Event‐based secure leader‐following consensus control for multiagent systems with multiple cyber attacks, IEEE Trans. Cybern. 51 (2020), no. 1, 162–173.
[20]
L. Marin, A. Soriano, M. Valles, A. Valera and A. Albertos. Event based distributed Kalman filter for limited resource multirobot cooperative localization, Asian J. Control 21 (2019), 1531–1546.
[21]
B. Shen, Z. Wang, and H. Qiao, Event‐triggered state estimation for discrete‐time multidelayed neural networks with stochastic parameters and incomplete measurements, IEEE Trans. Neural Netw. Learn. Syst. 28 (2016), no. 5, 1152–1163.
[22]
K. Sun, Q. Jianbin, H. R. Karimi, and Y. Fu, Event‐triggered robust fuzzy adaptive finite‐time control of nonlinear systems with prescribed performance, IEEE Trans. Fuzzy Syst. (2020).
[23]
G. Zong, H. Ren, and H. R. Karimi, Event‐triggered communication and annular finite‐time H filtering for networked switched systems, IEEE Trans. Cybern. 51 (2020), no. 1, 309–317.
[24]
A. Girard, Dynamic triggering mechanisms for event‐triggered control, IEEE Trans. Autom. Control 60 (2014), no. 7, 1992–1997.
[25]
Q. Li, B. Shen, Z. Wang, T. Huang and J. Luo. Synchronization control for a class of discrete time‐delay complex dynamical networks: A dynamic event‐triggered approach, IEEE Trans. Cybern. 49 (2018), no. 5, 1979–1986.
[26]
Q. Li, B. Shen, Z. Wang and W. Sheng. Recursive distributed filtering over sensor networks on gilbert‐elliott channels: A dynamic event‐triggered approach, Automatica 113 (2020), 108681.
[27]
Y. Liu, B. Shen, and H. Shu, Finite‐time resilient H state estimation for discrete‐time delayed neural networks under dynamic event‐triggered mechanism, Neural Netw. 121 (2020), 356–365.
[28]
D. Zhao, Z. Wang, Y. Chen and G. Wei. Proportional‐integral observer design for multidelayed sensor‐saturated recurrent neural networks: A dynamic event‐triggered protocol, IEEE Trans. Cybern. 50 (2020), no. 11, 4619–4632.
[29]
H. Dong, H. Dong, Z. Wang, D. W. C. Ho and H. Gao. Variance‐constrained H filtering for a class of nonlinear time‐varying systems with multiple missing measurements: The finite‐horizon case, IEEE Trans. Signal Process. 58 (2010), no. 5, 2534–2543.
[30]
L. Wang, Z. Wang, Q‐L. Han and G. Wei. Event‐based variance‐constrained H filtering for stochastic parameter systems over sensor networks with successive missing measurements, IEEE Trans. Cybern. 48 (2017), no. 3, 1007–1017.
[31]
J. Hu, Z. Wang, S. Liu and H. Gao. A variance‐constrained approach to recursive state estimation for time‐varying complex networks with missing measurements, Automatica 64 (2016), 155–162.
[32]
W. Li, Y. Jia, and J. Du, Variance‐constrained state estimation for nonlinearly coupled complex networks, IEEE Trans. Cybern. 48 (2017), no. 2, 818–824.
[33]
A. Schellenberg, W. Rosehart, and J. A. Aguado, Cumulant‐based stochastic nonlinear programming for variance constrained voltage stability analysis of power systems, IEEE Trans. Power Systems 21 (2006), no. 2, 579–585.
[34]
Z. Wang, D. W. C. Ho, and X. Liu, Variance‐constrained filtering for uncertain stochastic systems with missing measurements, IEEE Trans. Autom. control 48 (2003), no. 7, 1254–1258.
[35]
Y. Zhang, Z. Wang, and L. Ma, Variance‐constrained state estimation for networked multi‐rate systems with measurement quantization and probabilistic sensor failures, Int. J. Robust Nonlin. Control 26 (2016), no. 16, 3507–3523.
[36]
G. Campion, G. Bastin, and B. Dandrea‐Novel, Structural properties and classification of kinematic and dynamic models of wheeled mobile robots, IEEE Trans. Robot. Autom. 12 (1996), no. 1, 47–62.
[37]
G. Battistelli, L. Chisci, C. Fantacci, A. Farina and A. Graziano. A new approach for doppler‐only target tracking, Proceedings of the 16th International Conference on Information Fusion. IEEE, Istanbul, Turkey, 2013, pp. 1616–1623.
[38]
R. P. Guan, B. Ristic, L. Wang, B. Moran and R. Evans. Feature‐based robot navigation using a doppler‐azimuth radar, Int. J. Control 90 (2017), no. 4, 888–900.
[39]
Q. Liu, Z. Wang, X. He and D. H. Zhou. Event‐based recursive distributed filtering over wireless sensor networks, IEEE Trans. Autom. Control 60 (2015), no. 9, 2470–2475.
[40]
D. Ding et al., A survey on security control and attack detection for industrial cyber‐physical systems, Neurocomputing 275 (2018), 1674–1683.
[41]
L. Wang, B. Shen, Z. Wang, T. Huang and J. Luo. Synchronization control for a class of discrete‐time dynamical networks with packet dropouts: A coding‐decoding‐based approach, IEEE Trans. Cybern. 48 (2018), no. 8, 2437–2448.
[42]
L. Wang, Z. Wang, G. Wei and F. E. Alsaadi. Observer‐based consensus control for discrete‐time multiagent systems with coding‐decoding communication protocol, IEEE Trans. Cybern. 49 (2019), no. 12, 4335–4345.
[43]
S. Liu, Z. Wang, Y. Chen and G. Wei. Dynamic event‐based state estimation for delayed artificial neural networks with multiplicative noises: A gain‐scheduled approach, Neural Netw. 132 (2020), 211–219.
[44]
D. Ding, Z. Wang, and Q.‐L. Han, A set‐membership approach to event‐triggered filtering for general nonlinear systems over sensor networks, IEEE Trans. Autom. Control 65 (2020), no. 4, 1792–1799.

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  • (2023)Set membership filtering for discrete saturated time‐varying networked systems with incomplete measurements under round‐robin protocolAsian Journal of Control10.1002/asjc.313225:6(4721-4732)Online publication date: 16-Nov-2023

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              cover image Asian Journal of Control
              Asian Journal of Control  Volume 23, Issue 5
              September 2021
              491 pages
              ISSN:1561-8625
              EISSN:1934-6093
              DOI:10.1002/asjc.v23.5
              Issue’s Table of Contents
              This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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              John Wiley & Sons, Inc.

              United States

              Publication History

              Published: 01 October 2021

              Author Tags

              1. dynamic event‐triggered communication mechanism
              2. error variance constraint
              3. H filtering
              4. mobile robot localization
              5. nonlinear resilient filter

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              • (2023)Set membership filtering for discrete saturated time‐varying networked systems with incomplete measurements under round‐robin protocolAsian Journal of Control10.1002/asjc.313225:6(4721-4732)Online publication date: 16-Nov-2023

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