文章基本信息

标题：LabVIEW Perturbed Particle Swarm Optimization Based Approach for Model Predictive Control Tuning
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作者：Mohamed Lotfi Derouiche ; Soufiene Bouallègue ; Joseph Haggège 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2016
卷号：49
期号：5
页码：353-358
DOI：10.1016/j.ifacol.2016.07.138
语种：English
出版社：Elsevier
摘要：AbstractIn 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.
关键词：KeywordsModel Predictive Controlparameters tuning problemperturbed Particle Swarm OptimizationGenetic AlgorithmLabVIEW implementationMAGLEV system