Title :
Nonlinear Predictive Functional Control Based on Hopfield Network and its Application in CSTR
Author_Institution :
Dept. of Autom., North China Electr. Power Univ., Beijing
Abstract :
CSTR is a nonlinear chemical reactor widely used in chemical industry and can be simplified as an affine nonlinear system. Hopfield network is a neural network with rich dynamic characteristics. In this paper, affine nonlinear system is treated as black box, and is identified with Hopfield network. After obtaining the relative degree of the nonlinear system from the network, state feedback linearization method is used to transform CSTR to a one-order linear system. The state variables and Lie derivatives needed in the transform can be obtained from the Hopfield network. Finally, a PFC controller is designed to control the linear system. Simulations prove that the new method has good control performance
Keywords :
Hopfield neural nets; chemical engineering computing; chemical industry; chemical reactors; control engineering computing; control system synthesis; linear systems; nonlinear systems; predictive control; state feedback; Hopfield neural network; affine nonlinear system; chemical industry; continuous stirred tank reactor; linear control system; nonlinear chemical reactor; predictive functional control; state feedback linearization method; Chemical industry; Chemical reactors; Continuous-stirred tank reactor; Control systems; Hopfield neural networks; Linear systems; Neural networks; Nonlinear dynamical systems; Nonlinear systems; State feedback; Hopfield Network; continuous stirred tank reactor (CSTR); predictive functional control; state feedback linearization;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.258361
