TY - GEN
T1 - A neurodynamic optimization approach to nonlinear model predictive control
AU - Pan, Yunpeng
AU - Wang, Jun
PY - 2010
Y1 - 2010
N2 - This paper presents a recurrent neural network (RNN) approach to nonlinear model predictive control (MPC). By using decomposition, the original optimization associated with nonlinear MPC is reformulated as a quadratic programming problem with unknown parameters. We employ an RNN and develop a learning algorithm for solving the formulated problem. The proposed RNN approach has many desirable properties such as global convergence and low complexity. Finally, we apply the neurodynamic approach to mobile robot navigation to demonstrate its effectiveness and efficiency. ©2010 IEEE.
AB - This paper presents a recurrent neural network (RNN) approach to nonlinear model predictive control (MPC). By using decomposition, the original optimization associated with nonlinear MPC is reformulated as a quadratic programming problem with unknown parameters. We employ an RNN and develop a learning algorithm for solving the formulated problem. The proposed RNN approach has many desirable properties such as global convergence and low complexity. Finally, we apply the neurodynamic approach to mobile robot navigation to demonstrate its effectiveness and efficiency. ©2010 IEEE.
KW - Mobile robot navigation
KW - Nonlinear model predictive control
KW - Recurrent neural networks
UR - http://www.scopus.com/inward/record.url?scp=78751512666&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-78751512666&origin=recordpage
U2 - 10.1109/ICSMC.2010.5642367
DO - 10.1109/ICSMC.2010.5642367
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781424465880
SP - 1597
EP - 1602
BT - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
T2 - 2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010
Y2 - 10 October 2010 through 13 October 2010
ER -