Fault Estimation for Semi-Markov Jump Neural Networks Based on the Extended State Method

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Abstract
This paper addresses fault estimation in discrete-time semi-Markov jump neural networks (s-MJNNs) under the Round-Robin protocol and proposes an innovative extended state observer-based approach. Unlike studies considering only constant transition rates, this work investigates s-MJNNs with time-varying transition probabilities, which more closely reflect practical situations. By incorporating actuator and sensor faults as augmented state variables, an extended state observer is proposed to estimate system states and faults simultaneously. To alleviate network congestion and optimize communication resources, the Round-Robin protocol is adopted to schedule data transmission efficiently. By constructing a Lyapunov–Krasovskii functional and applying the discrete Wirtinger inequality, sufficient conditions are derived to ensure the mean square exponential stability and dissipative performance of the system. The observer gain parameters are computed using the linear matrix inequality (LMI) method. Numerical simulations validate the effectiveness and performance of the proposed fault estimation method.
Funding Information
  • Qingdao Agricultural University Doctoral Start-Up Funds (1122031, 1121005, XDB201815, HNZHNY-KFKT-202206, 2023TSGC0741, 32073029)
  • Talent Training Project of Heilongjiang Bayi Agricultural University (1122031, 1121005, XDB201815, HNZHNY-KFKT-202206, 2023TSGC0741, 32073029)
  • Open Project Program of Key Laboratory of Smart Agricultural Technology in Tropical South China, Ministry of Agriculture and Rural Affairs, P. R. China (1122031, 1121005, XDB201815, HNZHNY-KFKT-202206, 2023TSGC0741, 32073029)
  • 2023 Shandong Province Science and Technology based Small and Medium sized Enterprises Innovation Capability Enhancement Project (1122031, 1121005, XDB201815, HNZHNY-KFKT-202206, 2023TSGC0741, 32073029)
  • National Natural Science Foundation of China (1122031, 1121005, XDB201815, HNZHNY-KFKT-202206, 2023TSGC0741, 32073029)

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