TY - GEN
T1 - Two neural network approaches to model predictive control
AU - Pan, Yunpeng
AU - Wang, Jun
PY - 2008
Y1 - 2008
N2 - Model predictive control (MPC) is a powerful technique for optimizing the performance of control systems. However, the high computational demand in solving optimization problem associated with MPC in real-time is a major obstacle. Recurrent neural networks have various advantages in solving optimization problems. In this paper, we apply two recurrent neural network models for MPC based on linear and quadratic programming formulations. Both neural networks have good convergence performance and low computational complexity. A numerical example is provided to illustrate the effectiveness and efficiency of the proposed methods and show the different control behaviors of the two neural network approaches. ©2008 AACC.
AB - Model predictive control (MPC) is a powerful technique for optimizing the performance of control systems. However, the high computational demand in solving optimization problem associated with MPC in real-time is a major obstacle. Recurrent neural networks have various advantages in solving optimization problems. In this paper, we apply two recurrent neural network models for MPC based on linear and quadratic programming formulations. Both neural networks have good convergence performance and low computational complexity. A numerical example is provided to illustrate the effectiveness and efficiency of the proposed methods and show the different control behaviors of the two neural network approaches. ©2008 AACC.
UR - https://www.scopus.com/pages/publications/52449097557
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-52449097557&origin=recordpage
U2 - 10.1109/ACC.2008.4586734
DO - 10.1109/ACC.2008.4586734
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781424420797
SP - 1685
EP - 1690
BT - Proceedings of the American Control Conference
T2 - 2008 American Control Conference (ACC 2008)
Y2 - 11 June 2008 through 13 June 2008
ER -