Title :
Learning in multiple model adaptive control switch
Author :
Lourenço, J. M A ; Lemos, J.M.
Author_Institution :
Escola Superior de Tecnologia, Setubal, Portugal
fDate :
6/1/2006 12:00:00 AM
Abstract :
Including a learning mechanism in SMMAC (switched, multiple-model adaptive control) avoids the need for a priori knowledge of the model set of the plant to control and leads to a significant performance improvement with respect to the sole inclusion of an adaptive control channel in combination with switched fixed local controllers.
Keywords :
adaptive control; controllers; industrial control; learning (artificial intelligence); switches; SMMAC; adaptive control channel; learning mechanism; switched fixed local controllers; switched multiple-model adaptive control; Adaptive control; Control systems; Environmental economics; Instruments; Learning systems; Neuromuscular; Nonlinear dynamical systems; Surgery; Uncertain systems; Uncertainty;
Journal_Title :
Instrumentation & Measurement Magazine, IEEE
DOI :
10.1109/MIM.2006.1637975
