LabVIEW Perturbed Particle Swarm Optimization Based Approach for Model Predictive Control Tuning

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• 作者：Mohamed Lotfi Derouiche^* ; ^{mohamedlotfi.derouiche@enit.rnu.tn" class="auth_mail" title="E-mail the corresponding author} ; Soufiene Bouall&egrave ; gue^* ; ^{soufiene.bouallegue@issig.rnu.tn" class="auth_mail" title="E-mail the corresponding author} ; Joseph Hagg&egrave ; ge^* ; ^{joseph.haggege@enit.rnu.tn" class="auth_mail" title="E-mail the corresponding author} ; Guillaume Sandou^** ; ^{Guillaume.Sandou@centralesupelec.fr" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Model Predictive Control ; parameters tuning problem ; perturbed Particle Swarm Optimization ; Genetic Algorithm ; LabVIEW implementation ; MAGLEV system
• 刊名：IFAC-PapersOnLine
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：49
• 期：5
• 页码：353-358
• 全文大小：501 K

文摘

In this paper, a new Model Predictive Controller (MPC) parameters tuning strategy is proposed using a Lab VIEW-based perturbed Particle Swarm Optimization (pPSO) approach. This original LabVIEW implementation of this metaheuristic algorithm is firstly validated on some test functions in order to show its efficiency and validity. The optimization results are compared with the standard PSO approach. The parameters tuning problem, i.e. the weighting factors on the output error and input increments of the MPC algorithm, is then formulated and systematically solved, using the proposed LabVIEW pPSO algorithm. The case of a Magnetic Levitation (MAGLEV) system is investigated to illustrate the robustness and superiority of the proposed pPSO-based tuning MPC approach. All obtained simulation results, as well as the statistical analysis tests for the formulated control problem with and without constraints, are discussed and compared with the Genetic Algorithm Optimization (GAO)-based technique in order to improve the effectiveness of the proposed pPSO-based MPC tuning methodology.