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COMPARISON OF NGPC
WITH APPROXIMATE AND NONLINEAR Abstract: In this paper, linear and nonlinear predictive control techniques are applied to a typical nonlinear plant using NGPC. Due to the nonlinearities of the plant, by using instantaneous linearization of a nonlinear model incorporating the Neural Generalized Predictive control (NGPC), linear predictive control techniques can be easily applied. In order to validate the control performance of NGPC in terms of assessing the effects of linearization, nonlinearities into account is also applied to the plant. The control performance using both methods is examined and compared. The results illustrate both of them can provide the optimal performance for the plant and are robust. A NGPC algorithm is more suitable which demand less computation load compare to nonlinear predictor which can be crucial point of capability to use the controller in real-time applications. Keywords: Neural Generalized predictive control linear and nonlinear predictors, non-linear plant.
@ARTICLE{P1110609003,
AUTHOR = {D.N.Rao
and M.R.K.Murthy and S.R.M.Rao and
D.N.Harshal},
TITLE = {COMPARISON OF NGPC WITH APPROXIMATE AND NONLINEAR PREDICTIVE CONTROL - A SIMULATION STUDY }, JOURNAL = {ICGST International Journal on Automatic Control and Systems Engineering, ACSE}, YEAR = {2006}, VOLUME = {06}, ISSUE = {I}, PAGES = {35--40} }
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