TY - GEN
T1 - Adaptive neural-network predictive control for nonminimum-phase systems
AU - Wu, Wei
AU - Hsu, Wei Ching
PY - 2006/12/1
Y1 - 2006/12/1
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 for neural network architecture associated with prescribed input/output patterns, the feedforward neural network (FNN) is used to capture dynamic and steady-state characteristics of minimum-phase modes over a specified operating range. A one-step-ahead neural prediction algorithm with respect to physical constraints can carry out the offset free performance. Closed-loop simulations demonstrate the effectiveness of the proposed approaches.
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 for neural network architecture associated with prescribed input/output patterns, the feedforward neural network (FNN) is used to capture dynamic and steady-state characteristics of minimum-phase modes over a specified operating range. A one-step-ahead neural prediction algorithm with respect to physical constraints can carry out the offset free performance. Closed-loop simulations demonstrate the effectiveness of the proposed approaches.
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M3 - Conference contribution
AN - SCOPUS:34047207792
SN - 1424402107
SN - 9781424402106
T3 - Proceedings of the American Control Conference
SP - 2981
EP - 2986
BT - Proceedings of the 2006 American Control Conference
T2 - 2006 American Control Conference
Y2 - 14 June 2006 through 16 June 2006
ER -