Title :
Neural network based minimal state-space representation of nonlinear MIMO systems for feedback control
Author :
Vassiljeva, Kristina ; Petlenkov, Eduard ; Belikov, Juri
Author_Institution :
Dept. of Comput. Control, Tallinn Univ. of Technol., Tallinn, Estonia
Abstract :
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.
Keywords :
MIMO systems; autoregressive processes; discrete time systems; feedback; linearisation techniques; neurocontrollers; nonlinear control systems; state-space methods; additive nonlinear autoregressive exogenous model; discrete time state space form; dynamic feedback; feedback control; minimal state space representation; neural network; nonlinear MIMO system; nonlinear multiinput multioutput system; simplified additive NARX model; Artificial neural networks; Control systems; Equations; Heuristic algorithms; MIMO; Mathematical model; Nonlinear dynamical systems; ANARX model; feedback linearization; neural networks; nonlinear control systems; state-space control;
Conference_Titel :
Control Automation Robotics & Vision (ICARCV), 2010 11th International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-7814-9
DOI :
10.1109/ICARCV.2010.5707311
