Abstract
An adaptive predictive control algorithm of nonlinear non-minimum phase systems using neural network is proposed. The nonlinear system is separated into linear non-minimum phase system and nonlinear parts by Taylor series expansion. The resulting nonlinear part is identified by a neural network and compensated in the control algorithm such that feedback linearization can be achieved. A modified neural network composed of linear neural network (LNN) which represent the linearized model at the operating point and a multilayered feedforward neural network which approximate the nonlinear dynamics that cannot be modeled by the LNN is utilized in this investigation.
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