Model reference control of nonlinear systems by dynamic output feedback linearization of neural network based ANARX models

A dynamic output feedback linearization technique for model reference control of nonlinear systems identified by an additive nonlinear autoregressive exogenous (ANARX) model. ANARX structure of the model can be obtained by training a neural network of the specific restricted connectivity structure. Linear discrete time reference model is given in the form of transfer function defining desired zeros and poles of the closed loop system. NN-based ANARX model can be linearized by the proposed linearization algorithm thus that the transfer function of the linear closed loop system corresponds to the given reference model. The proposed linearization algorithm can be applied to control of a wide class of nonlinear SISO and MIMO systems. The effectiveness of the proposed control technique is demonstrated on numerical example.