TY - GEN
T1 - Improving distributed model predictive control performance via weight optimization using PSO
AU - McNamara, Paul
AU - Lightbody, Gordon
PY - 2009
Y1 - 2009
N2 - In recent years there has been much research performed in developing Distributed Model Predictive Control (DMPC) techniques which allow a Model Predictive Control (MPC) scheme to be distributed amongst a number of agents. By optimizing the weights in an MPC system, performance can be improved. In this paper, a PSO based weight optimization method for a DMPC system is developed and it is shown how DMPC performance can be optimized whilst constraining the number of iterations of the optimization algorithm.
AB - In recent years there has been much research performed in developing Distributed Model Predictive Control (DMPC) techniques which allow a Model Predictive Control (MPC) scheme to be distributed amongst a number of agents. By optimizing the weights in an MPC system, performance can be improved. In this paper, a PSO based weight optimization method for a DMPC system is developed and it is shown how DMPC performance can be optimized whilst constraining the number of iterations of the optimization algorithm.
KW - Distributed model predictive control
KW - Distributed optimization
KW - Load frequency control
KW - Particle swarm optimization
UR - https://www.scopus.com/pages/publications/79955716195
U2 - 10.3182/20090921-3-TR-3005.00032
DO - 10.3182/20090921-3-TR-3005.00032
M3 - Conference proceeding
AN - SCOPUS:79955716195
SN - 9783902661661
T3 - IFAC Proceedings Volumes (IFAC-PapersOnline)
BT - 2nd IFAC International Conference on Intelligent Control Systems and Signal Processing
PB - IFAC Secretariat
ER -