Control distributed parameter systems with orthogonal neural network learning

Article type: Research Article

Authors: Garliauskas, Algis | Gupta, Madan M.

Affiliations: Institute of Mathematics and Informatics, Akademijos 4, 2600 Vilnius, Lithuania. E-mail: [email protected] | University of Saskatchewan, Saskatoon, SK, Canada, S7N 5A9

Abstract: Adaptive Control Distributed Parameter Systems (ACDPS) with adaptive learning algorithms based on orthogonal neural network methodology are presented in this paper. We discuss a modification of orthogonal least squares learning to find appropriate efficient algorithms for solution of ACDPS problems. A two times problem linked with the real time of plant control dynamic processes and the learning time for adjustment of parameters in adaptive control of unknown distributed systems is discussed. The simulation results demonstrate that the orthogonal learning algorithms on a neural network concept allow to find perfectly tracked output control distributed parameters in ACDPS and have rather a good perspective in the development of generalised ACDSP theory and practice in the future.

Keywords: adaptive control, distributed parameter system, orthogonal neural network learning, nonlinear control

DOI: 10.3233/INF-1996-7403

Journal: Informatica, vol. 7, no. 4, pp. 431-454, 1996