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- W2002853564 abstract "In this paper, stable indirect adaptive control with recurrent neural networks is presented for square multivariable non-linear plants with unknown dynamics. The control scheme is made of an adaptive instantaneous neural model, a neural controller based on fully connected “Real-Time Recurrent Learning” (RTRL) networks and an online parameters updating law. Closed-loop performances as well as sufficient conditions for asymptotic stability are derived from the Lyapunov approach according to the adaptive updating rate parameter. Robustness is also considered in terms of sensor noise and model uncertainties. The control scheme is then applied to the Tennessee Eastman Challenge Process in order to illustrate the efficiency of the proposed method for real-world control problems." @default.
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- W2002853564 date "2009-06-01" @default.
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- W2002853564 title "Stable adaptive control with recurrent neural networks for square MIMO non-linear systems" @default.
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- W2002853564 doi "https://doi.org/10.1016/j.engappai.2008.12.005" @default.
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