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• W2113756475 abstract "In this paper, we are dealing with the problem of controlling an unknown nonlinear dynamical system. The algorithm is divided into two phases. First a dynamical neural network identifier is employed to perform black box identification and then a dynamic state feedback is developed to appropriately control the unknown system. We apply the algorithm to control the speed of a nonlinearized DC motor, giving in this way an application insight. In the algorithm, not all the plant states are assumed to be available for measurement.< <ETX xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>></ETX>" @default.
• W2113756475 created "2016-06-24" @default.
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• W2113756475 date "1994-03-01" @default.
• W2113756475 modified "2023-10-17" @default.
• W2113756475 title "Adaptive control of unknown plants using dynamical neural networks" @default.
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• W2113756475 doi "https://doi.org/10.1109/21.278990" @default.
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