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In this paper, adaptive prescribed performance output feedback control is investigated for a class of nonlinear systems with unmodeled dynamics. Neural networks are used to approximate the unknown nonlinear functions. MT-filters are employed to estimate the unmeasured states. The unmodeled dynamics is dealt with by introducing an available dynamic signal. Adaptive output feedback dynamic surface control...
In this work a model predictive control approach based on a neural network Wiener model is developed and applied for an intensified continuous reactor. The Wiener model is constituted by two parts: a linear state space identified model based on nonlinear first-principle model, a nonlinear neural network model developed to predict the nonlinear controlled output. Next, a local linearization of neural...
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