TY - GEN
T1 - A neurodynamic optimization approach to robust pole assignment for synthesizing linear state feedback control systems
AU - Le, Xinyi
AU - Wang, Jun
PY - 2013
Y1 - 2013
N2 - This paper presents a neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems via state feedback. A pseudoconvex objective function is minimized as a robustness measure. A neurodynamic model is applied whose global convergence was theoretically proved for constrained pseudoconvex optimization. Compared with existing approaches on benchmark problems, the convergence of proposed neurodynamic approach to global optimal solutions can be guaranteed. Simulation results of the proposed neurodynamic approach is reported to demonstrate its superiority. © 2013 IEEE.
AB - This paper presents a neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems via state feedback. A pseudoconvex objective function is minimized as a robustness measure. A neurodynamic model is applied whose global convergence was theoretically proved for constrained pseudoconvex optimization. Compared with existing approaches on benchmark problems, the convergence of proposed neurodynamic approach to global optimal solutions can be guaranteed. Simulation results of the proposed neurodynamic approach is reported to demonstrate its superiority. © 2013 IEEE.
UR - http://www.scopus.com/inward/record.url?scp=84902335865&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84902335865&origin=recordpage
U2 - 10.1109/CDC.2013.6760967
DO - 10.1109/CDC.2013.6760967
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781467357173
SP - 6806
EP - 6811
BT - Proceedings of the IEEE Conference on Decision and Control
PB - IEEE
T2 - 52nd IEEE Conference on Decision and Control, CDC 2013
Y2 - 10 December 2013 through 13 December 2013
ER -