Title :
Control of nonlinear multivariable systems using a dynamic neural network
Author :
Rao, D.H. ; Wood, H.C. ; Gupta, M.M.
Author_Institution :
Coll. of Eng., Saskatchewan Univ., Saskatoon, Sask., Canada
fDate :
27 Jun-2 Jul 1994
Abstract :
A complex control system, in general, consists of two or more independently designed and mutually affecting subsystems. Proper coordination and control of multiple subsystems is responsible for the overall functioning of the system. This necessitates the development of control schemes for multivariable systems. This is a formidable task; more so if the systems involved are nonlinear with unknown dynamics. Because of their parallelism, functional approximation and learning capabilities, artificial neural networks can be effectively employed to control multivariable systems. The intent of this paper is to describe a neural network called the dynamic neural processor (DNP), and to use this structure to control nonlinear multivariable systems. The DNP is a dynamic neural network developed based on the concept of neural subpopulations which is in sharp contrast with the conventionally assumed structure of artificial neural networks
Keywords :
multivariable control systems; neural nets; nonlinear control systems; complex control system; dynamic neural network; functional approximation; learning capabilities; nonlinear multivariable systems; parallelism; Artificial neural networks; Biological neural networks; Control systems; Design engineering; MIMO; Neural networks; Neurons; Nonlinear control systems; Nonlinear dynamical systems; Parallel processing;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374616