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Event-triggered H ∞ state estimation for discrete-time neural networks with mixed time delays and sensor saturations

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Abstract

In this paper, the event-triggered H ∞ state estimation problem is investigated for a class of neural networks with mixed time delays and sensor saturations. The mixed time delays consist of discrete and distributed delays. The measurement outputs are subject to the sensor saturations due to the physical constraints. Through the available measurement outputs, the main purpose of the addressed problem is to design a state estimator to estimate the actual neural states. In order to improve the efficiency in resource utilization, an event-triggered mechanism is employed to decide whether the received measurement output is transmitted to the state estimator. Different from the existing event-triggering strategies, the triggering condition is given for each sensor, and the measurement output from each sensor is sent according to their separate triggering conditions. By using the Lyapunov functional approach, sufficient conditions are derived to guarantee that the estimation error dynamics is exponentially stable and the H ∞ performance requirement is satisfied. Then, the desired H ∞ state estimator is designed in terms of the solution to a linear matrix inequality that can be easily solved by the MATLAB toolboxes. Finally, one simulation example is provided to show the effectiveness of the proposed event-triggered estimation scheme.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grants 61473076 and 61374010, the Shu Guang project of Shanghai Municipal Education Commission and Shanghai Education Development Foundation under Grant 13SG34, the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, the Fundamental Research Funds for the Central Universities under Grant CUSF-DH-D-2016046, the DHU Distinguished Young Professor Program, and the National Priority Research Project NPRP 4-1162-1-181 funded by Qatar National Research Fund.

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Authors and Affiliations

  1. School of Information Science and Technology, Donghua University, Shanghai, 201620, China

    Qi Li & Bo Shen

  2. Department of Mathematics, Yangzhou University, Yangzhou, 225002, China

    Yurong Liu

  3. Department of Mathematics, Texas A&M University at Qatar, P.O. Box 23874, Doha, Qatar

    Tingwen Huang

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  1. Qi Li
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  2. Bo Shen
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  3. Yurong Liu
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  4. Tingwen Huang
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Correspondence to Bo Shen.

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Li, Q., Shen, B., Liu, Y. et al. Event-triggered H ∞ state estimation for discrete-time neural networks with mixed time delays and sensor saturations. Neural Comput & Applic 28, 3815–3825 (2017). https://doi.org/10.1007/s00521-016-2271-2

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  • DOI: https://doi.org/10.1007/s00521-016-2271-2

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