State-space control of nonlinear systems identified by ANARX and Neural Network based SANARX models

A state-space technique for control of nonlinear SISO systems identified by an Additive Nonlinear Autoregressive eXogenous (ANARX) model is presented. Two cases are shown. In the first case system model is given explicitly in the form of ANARX structure. In the second case controlled system is identified by Neural Network based Simplified Additive NARX (NN-SANARX) model linearized by dynamic feedback. The neural network based model is represented in the discrete-time state-space form. The effectiveness of the approach proposed in the paper is demonstrated on numerical examples with SISO and MIMO systems.

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