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

Mohamed Derouiche 1, 2 Soufiene Bouallègue 1 Joseph Haggège 1 Guillaume Sandou 3, 2
1 ENIT - Ecole Nationale d'Ingénieurs de Tunis
2 L2S - Laboratoire des signaux et systèmes
3 DISCO - Dynamical Interconnected Systems in COmplex Environments
L2S - Laboratoire des signaux et systèmes, Inria Saclay - Ile de France
Abstract : In this paper, a new Model Predictive Controller (MPC) parameters tuning strategy is proposed using a LabVIEW-based perturbed Particle Swarm Algorithm (pPSA). This original LabVIEW implementation of this metaheuristic algorithm is rstly 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 pPSA algorithm. The case of a Magnetic Levitation (MAGLEV) system is investigated to illustrate the robustness and superiority of the proposed pPSA-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 pPSA-based MPC tuning methodology.
Keywords : Genetic Algorithm perturbed Particle Swarm Optimization parameters tuning problem MAGLEV system. Model Predictive Control LabVIEW implementation
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Conference papers
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https://hal-centralesupelec.archives-ouvertes.fr/hal-01347041
Contributor : Guillaume Sandou <>
Submitted on : Monday, July 25, 2016 - 3:08:47 PM
Last modification on : Friday, December 20, 2019 - 11:48:05 AM

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Mohamed Derouiche, Soufiene Bouallègue, Joseph Haggège, Guillaume Sandou. LabVIEW Perturbed Particle Swarm Optimization Based Approach for Model Predictive Control Tuning. 4th IFAC International Conference on Intelligent Control and Automation Sciences (ICONS 2016), Jun 2016, Reims, France. ⟨hal-01347041⟩

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