Title :
Online Decoupling and Control of Multivariable Nonlinear System Based on Neural Network
Author :
Li, X.L. ; Yang, L. ; Bai, Y.
Author_Institution :
North China Electr. Power Univ., Baoding
Abstract :
Aimed at neural network can approach any nonlinear dynamic system with arbitrary accuracy, the frame of distributed NN decoupling system are proposed to decouple the MIMO nonlinear system. The learning algorithm of NN is online, which makes a set of cross-correlation function as the target function, and adopts hybrid genetic algorithm to train neural decoupler. When the decoupling is completed, the single nerve cell self-adaptor PID is used to control two SISO systems. The efficiency of the algorithm has been shown by numerical simulations combining nonlinear system
Keywords :
control engineering computing; genetic algorithms; multivariable control systems; neurocontrollers; nonlinear control systems; numerical analysis; three-term control; SISO systems; cross-correlation function; distributed neural network decoupling system; hybrid genetic algorithm; multivariable nonlinear system control; neural decoupler; neural network; nonlinear dynamic system; numerical simulations; online decoupling; self-adaptor PID; target function; Automatic control; Control systems; Distributed control; Genetic algorithms; MIMO; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Vehicle dynamics; Decoupling; Genetic algorithm; Neural network; Nonlinear system; Online learning;
Conference_Titel :
Industrial Electronics and Applications, 2006 1ST IEEE Conference on
Conference_Location :
Singapore
Print_ISBN :
0-7803-9513-1
Electronic_ISBN :
0-7803-9514-X
DOI :
10.1109/ICIEA.2006.257152
