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

Abstract

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.