Neural network based minimal state-space representation of nonlinear mimo systems for feedback control
K Vassiljeva, E Petlenkov… - 2010 11th International …, 2010 - ieeexplore.ieee.org
2010 11th International Conference on Control Automation Robotics …, 2010•ieeexplore.ieee.org
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 of finding the
minimal state-space representation is considered.
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 of finding the
minimal state-space representation is considered.
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 of finding the minimal state-space representation is considered.
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