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• W2469834912 abstract "An adaptive input-output linearization method for general nonlinear systems is developed without using states of the system. Another key feature of this structure is the fact that, it does not need model of the system. In this scheme, neurolinearizer has few weights, so it is practical in adaptive situations. Online training of neurolinearizer is compared to model predictive recurrent training. Relationships between this controller and neural network based model reference adaptive controller are established. A CSTR reactor and pH control in a neutralization process illustrate performance of this method. Simulation studies show a superior performance with respect to a PI controller." @default.
• W2469834912 created "2016-07-22" @default.
• W2469834912 creator A5026257554 @default.
• W2469834912 date "2009-01-01" @default.
• W2469834912 modified "2023-09-27" @default.
• W2469834912 title "Real-Time Output Feedback Neurolinearization" @default.
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