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A state-space technique for control of nonlinear multi-input multi-output (MIMO) systems identified by an Additive Nonlinear Autoregressive eXogenous (ANARX) model is presented. 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 problem...
For the best model identification a set of neural networks (NNs) must be trained. First of all it is necessary to obtain the optimal structure of the NN. In addition a good choice of the initial values of the NN parameters can be of tremendous help in a successful control application. Further fit of the model is evaluated using several control criteria, and the optimal among them is selected. This...
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...
In this paper, an application of Neural Networks based Additive Nonlinear AutoRegressive eXogenous (NN-ANARX) structure is investigated for modeling and control of nonlinear multi-input-multi-output (MIMO) systems. A novel analytical technique for calculation of control signal is proposed. After that the ANARX-based dynamic output feedback linearization control algorithm is applied for control of...
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...
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