TY - GEN
T1 - Effects of parameter values and noise on PSO-based predictive control
T2 - An empirical study
AU - Yousuf, Muhammad S.
AU - Al-Duwaish, Hussain N.
PY - 2011
Y1 - 2011
N2 - In this paper, a Particle Swarm Optimization (PSO) based Model Predictive Control (MPC) scheme is studied through a variety of tests to better understand its behavior and characteristics. The technique has already been presented in the literature. Here, the PSO and MPC parameters are varied to study the effects on the quality of control and system dynamics. Model mismatch and noise are also introduced to test the controller performance. The results from various tests are compared and conclusions are drawn.
AB - In this paper, a Particle Swarm Optimization (PSO) based Model Predictive Control (MPC) scheme is studied through a variety of tests to better understand its behavior and characteristics. The technique has already been presented in the literature. Here, the PSO and MPC parameters are varied to study the effects on the quality of control and system dynamics. Model mismatch and noise are also introduced to test the controller performance. The results from various tests are compared and conclusions are drawn.
UR - https://www.scopus.com/pages/publications/79961188693
U2 - 10.1109/CICA.2011.5945743
DO - 10.1109/CICA.2011.5945743
M3 - Conference contribution
AN - SCOPUS:79961188693
SN - 9781424499038
T3 - IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CICA 2011 - 2011 IEEE Symposium on Computational Intelligence in Control and Automation
SP - 157
EP - 162
BT - IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CICA 2011 - 2011 IEEE Symposium on Computational Intelligence in Control and Automation
ER -