Title :
Indirect adaptive control of discrete DARMA systems using neural networks
Author_Institution :
Dept. de Electr. y Electron., Pais Vasco Univ., Bilbao, Spain
fDate :
27 Jun-2 Jul 1994
Abstract :
A neural network controller which is used for controlling unknown discrete-time DARMA systems is described. In a first stage, a two-layered neural network is used to estimate the unknown plant dynamics. The Widrow-Hoff delta rule is used as the learning algorithm for this network so as to minimize the difference between the plant actual response and that predicted by the neural network. In a second stage, the control law is generated online using a second two-layered neural network so that the plant output is brought to a desired reference signal. Simulation examples are presented to evaluate the design
Keywords :
adaptive control; autoregressive moving average processes; closed loop systems; discrete time systems; dynamics; feedforward neural nets; learning (artificial intelligence); Widrow-Hoff delta rule; closed loop system; discrete DARMA systems; indirect adaptive control; learning algorithm; two-layered neural network; Adaptive control; Control systems; Neural networks; Polynomials; Programmable control; Signal design; Signal generators; System identification; Upper bound;
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.1994.374624
