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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 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 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.

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This paper deals with the development and simulation of a MATLAB/Simulink model of a Tidal Stream Converter (TSC) to be installed in the breakwater at the harbour of Mutriku on the Basque coast in Spain. The developed model is that of a threebladed tidal turbine connected to a Doubly Fed Induction Generator (DFIG) for marine energy convertion. A Software-In-the-Loop (SIL) simulation of the established TSC model has been investigated using the NI VeriStand tool. This is achieved in order to prepare for the Hardware-In-the-Loop (HIL) implementation of the studied marine energy converter based on an NI Compact RIO real-time target. All simulation results, obtained by MATLAB/Simulink within a Model-In-the Loop (MIL) simulation framework and by NI VeriStand within SIL simulation one, are analyzed and compared in order to validate the developed TSC model.