TY - JOUR
T1 - Robust approximate pole assignment for second-order systems
T2 - Neural network computation
AU - Ho, Daniel W. C.
AU - Lam, James
AU - Xu, Jinhua
PY - 1998/11
Y1 - 1998/11
N2 - A recurrent neural network approach to robust approximate pole assignment for second-order systems is proposed. The design is formulated as an unconstrained optimization problem and solved via the gradient-flow approach, which is ideally suited for neural network implementation. Convergence of the gradient flow also is established. Simulation results are used to demonstrate the effectiveness of the proposed method.
AB - A recurrent neural network approach to robust approximate pole assignment for second-order systems is proposed. The design is formulated as an unconstrained optimization problem and solved via the gradient-flow approach, which is ideally suited for neural network implementation. Convergence of the gradient flow also is established. Simulation results are used to demonstrate the effectiveness of the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=0032207947&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0032207947&origin=recordpage
U2 - 10.2514/2.4326
DO - 10.2514/2.4326
M3 - RGC 21 - Publication in refereed journal
SN - 0731-5090
VL - 21
SP - 923
EP - 928
JO - Journal of Guidance, Control, and Dynamics
JF - Journal of Guidance, Control, and Dynamics
IS - 6
ER -