TY - JOUR
T1 - Adaptive neural-network predictive control for nonminimum-phase nonlinear processes
AU - Wu, Wei
AU - Hsu, Wei Ching
N1 - Funding Information:
This work was supported by the National Science Council of Taiwan under grant number NSC92-2214-E-224-002.
PY - 2007/2
Y1 - 2007/2
N2 - An adaptive neural-network predictive control strategy for a class of nonlinear processes, which exhibit input multiplicities and change in the sign of steady-state gains, is presented. According to the graphic-based determination associated with prescribed input/output patterns, the feed-forward neural network (FNN) is attributed to reconstruct dynamic and steady-state characteristics of minimum-phase modes with specified operating ranges. The flexible predictive control strategy using on-line neuro-based adaptation is developed for enhancing the predictive capability of neural network. Finally, the proposed FNN-based implementation is illustrated on simulations of both isothermal and adiabatic CSTR systems.
AB - An adaptive neural-network predictive control strategy for a class of nonlinear processes, which exhibit input multiplicities and change in the sign of steady-state gains, is presented. According to the graphic-based determination associated with prescribed input/output patterns, the feed-forward neural network (FNN) is attributed to reconstruct dynamic and steady-state characteristics of minimum-phase modes with specified operating ranges. The flexible predictive control strategy using on-line neuro-based adaptation is developed for enhancing the predictive capability of neural network. Finally, the proposed FNN-based implementation is illustrated on simulations of both isothermal and adiabatic CSTR systems.
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U2 - 10.1080/00986440600716043
DO - 10.1080/00986440600716043
M3 - Article
AN - SCOPUS:33750155235
VL - 194
SP - 177
EP - 193
JO - Chemical Engineering Communications
JF - Chemical Engineering Communications
SN - 0098-6445
IS - 2
ER -