TY - JOUR
T1 - Constrained neural control for the adaptive tracking of power profiles in a triga reactor
AU - Perez-Cruz, J. Humberto
AU - Chairez, Isaac
AU - Poznyak, Alexander
AU - de Rubio, Jose Jesus
PY - 2011/7
Y1 - 2011/7
N2 - In this study, a control scheme to accomplish the tracking of power profiles in a TRIGA Reactor is presented. This scheme permits the fulfillment of the inverse period constraint. Additionally, it eliminates the need for a physical model of the plant. Closed-loop identification of the nuclear system is carried out based only on external reactivity and neutron power by a differential neural network. This network utilizes a new learning law by which it is possible to guarantee the boundedness for weights and identification error. Once a neural model of the reactor is obtained, inverse period constraint can be expressed as a new constraint on the control input (external reactivity). However, an error term must be calculated to determine the new admissible set of control signal. This difficulty is overcome by using the sliding mode technique. Finally, a new control law is proposed. The effectiveness of this procedure is illustrated by numeric simulation.
AB - In this study, a control scheme to accomplish the tracking of power profiles in a TRIGA Reactor is presented. This scheme permits the fulfillment of the inverse period constraint. Additionally, it eliminates the need for a physical model of the plant. Closed-loop identification of the nuclear system is carried out based only on external reactivity and neutron power by a differential neural network. This network utilizes a new learning law by which it is possible to guarantee the boundedness for weights and identification error. Once a neural model of the reactor is obtained, inverse period constraint can be expressed as a new constraint on the control input (external reactivity). However, an error term must be calculated to determine the new admissible set of control signal. This difficulty is overcome by using the sliding mode technique. Finally, a new control law is proposed. The effectiveness of this procedure is illustrated by numeric simulation.
KW - Constrained control
KW - Differential neural network
KW - Nuclear research reactor
UR - http://www.scopus.com/inward/record.url?scp=79959869645&partnerID=8YFLogxK
M3 - Artículo
SN - 1349-4198
VL - 7
SP - 4575
EP - 4788
JO - International Journal of Innovative Computing, Information and Control
JF - International Journal of Innovative Computing, Information and Control
IS - 7 B
ER -