期刊
ISA TRANSACTIONS
卷 50, 期 2, 页码 177-194出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2010.12.007
关键词
Adaptive recursive least squares; Generalized predictive control; Identification; Neural networks; Nonlinear adaptive model predictive control
This paper presents two new adaptive model predictive control algorithms, both consisting of an on-line process identification part and a predictive control part. Both parts are executed at each sampling instant. The predictive control part of the first algorithm is the Nonlinear Model Predictive Control strategy and the control part of the second algorithm is the Generalized Predictive Control strategy. In the identification parts of both algorithms the process model is approximated by a series-parallel neural network structure which is trained by a recursive least squares (ARLS) method. The two control algorithms have been applied to: 1) the temperature control of a fluidized bed furnace reactor (FBFR) of a pilot plant and 2) the auto-pilot control of an F-16 aircraft. The training and validation data of the neural network are obtained from the open-loop simulation of the FBFR and the nonlinear F-16 aircraft models. The identification and control simulation results show that the first algorithm outperforms the second one at the expense of extra computation time. (C) 2010 ISA. Published by Elsevier Ltd. All rights reserved.
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