Robust control of dynamical systems using neural networks with input–output feedback linearization

This paper presents a control algorithm that combines three valuable features in robust and non-linear control, namely modelling using neural networks, input–output feedback linearization and LMI-based robust controller design. In the first step of the algorithm an affine description of a feedforward neural network model is derived. By performing an input–output feedback (IOF) linearization an uncertainty description of the IOF linearized system is derived based on the parametric uncertainties of the affine model. Then the LMI-based robust controller is designed by means of an optimization procedure. A key step in this procedure is the derivation of a polytopic boundary for the state-space matrices of the IOF linearized system based on the estimated parameters of the neural network and their uncertainty bounds.

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