Abstract
In this paper, a novel fault estimation methodology is proposed for a class of interconnected nonlinear continues-time systems with triangular forms. In the distributed fault estimation architecture, a fault detector is utilized to generate a residual between the subsystem and its detector or observer. Moreover, a threshold for distributed fault detection and estimation in each subsystem is designed. Due to the universal approximation capabilities of the radial basis function neural networks, it is used to estimate the unknown fault dynamics. The time-to-failure is determined by solving the adaptive law from the current time instant to a failure threshold. Finally, the proposed methods are verified in the simulation.
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61074073 and 61034005), the Fundamental Research Funds for the Central Universities (Grant Nos. N130504002 and N130104001), and SAPI Fundamental Research Funds (Grant No. 2013ZCX01).
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© 2014 Springer International Publishing Switzerland
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Liu, L., Wang, Z., Liu, J., Liu, Z. (2014). Neural-Network-Based Adaptive Fault Estimation for a Class of Interconnected Nonlinear System with Triangular Forms. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science(), vol 8866. Springer, Cham. https://doi.org/10.1007/978-3-319-12436-0_13
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DOI: https://doi.org/10.1007/978-3-319-12436-0_13
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