Title :
State feedback control supported by learning function based on neural network
Author :
Fujiwara, Toshitaka ; Miyakawa, Hirokazu
Author_Institution :
Mitsubishi Heavy Ind. Ltd., Hyogo, Japan
fDate :
28 Oct-1 Nov 1991
Abstract :
The authors propose a state feedback control system supported by a self-learning system, considering its application to a heat exchanger. The system fractionalizes the heat exchanger along the direction of flow, calculates the state variables for each fraction segment using mathematical models, and feeds back those variables. This system simplifies the control law and makes possible the use of nonlinear mathematical models. In order to deal with the problem of performance drift of the heat exchanger, two automatic learning functions supported by a neural network are built into the system: the automatic mathematical model learning function and the automatic state feedback gain learning function. The effects of these ideas were demonstrated in sample calculations and in a simulator of an actual ultra-super critical pressure boiler
Keywords :
feedback; heat exchangers; learning systems; neural nets; flow direction fractionalization; fraction segment; heat exchanger; learning function; neural network; nonlinear mathematical models; self-learning system; state feedback control; state variables; ultra-super critical pressure boiler; Automatic control; Control system synthesis; Control systems; Feeds; Mathematical model; Neural networks; Nonlinear control systems; Performance gain; State feedback; Temperature control;
Conference_Titel :
Industrial Electronics, Control and Instrumentation, 1991. Proceedings. IECON '91., 1991 International Conference on
Conference_Location :
Kobe
Print_ISBN :
0-87942-688-8
DOI :
10.1109/IECON.1991.239121