Multivariable Generalized Predictive Control Using An Improved Particle Swarm Optimization Algorithm

In this paper, an improvement of the particle swarm optimization (PSO) algorithm is proposed. The aim of this algorithm is to iteratively resolve the cost problem of the Multivariable Generalized Predictive Control (MGPC) method under multiple constraints previously reduced. An ill-conditioned chemical process modelled by an uncertain Multi-Input & Multi-Output (MIMO) plant is controlled in order to verify the validity and the effectiveness of the proposed algorithm. The performances obtained are compared with those given by the MGPC method using the standard PSO algorithm. The simulation results shows that the proposed algorithm outperforms standard PSO algorithm in terms of performance and robustness.