Effects of parameter values and noise on PSO-based predictive control: An empirical study

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...

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Bibliographic Details
Main Authors: Yousuf, M. S., Al-Duwaish, H. N.
Format: Conference Proceeding
Language:English
Subjects:
Automatic generation control
Cost function
Noise
Optimal control
Particle swarm optimization
Process control
Time frequency analysis
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Summary: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.
ISSN:2328-1448
DOI:10.1109/CICA.2011.5945743