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State and fault estimation for nonlinear recurrent neural network systems: Experimental testing on a three‐tank system
Author(s) -
Zhang Xiaoxiao,
Feng Xuexin,
Mu Zonglei,
Wang Youqing
Publication year - 2020
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.23714
Subject(s) - control theory (sociology) , correctness , observer (physics) , nonlinear system , actuator , artificial neural network , convergence (economics) , state (computer science) , fault (geology) , recurrent neural network , fault detection and isolation , linear matrix inequality , computer science , engineering , control engineering , mathematics , algorithm , artificial intelligence , control (management) , mathematical optimization , physics , quantum mechanics , seismology , geology , economics , economic growth
An observer is presented for the simultaneous estimation of the system state and actuator and sensor faults of a discrete recurrent neural network (RNN) system. The presented approach enables disturbance attenuation and guarantees observer convergence. First, the discrete RNN is converted to a discrete linear parameter varying (LPV) model. Then, the LPV model is further transformed into a descriptor system by extending the system state and sensor fault. Next, an H ∞ observer is presented for the simultaneous estimation of the extended state and actuator fault of the descriptor system. Finally, the problem of observer design is translated into solving a linear matrix inequality. Experimental tests on a three‐tank system have validated the effectiveness and correctness of the presented method.