NMPC via qLPV models and Taylor-based Scheduling Parameter Extrapolation: A Cartesian Robot Case Study
Résumé
In this brief paper, we present an overview of recent advances on Model Predictive Control (MPC) synthesis for nonlinear systems using quasi-Linear Parameter Varying (qLPV) embeddings. For such, we consider a highly nonlinear Cartesian robot benchmark as a case study. Specifically, we advocate on the use of recursive Taylor-based extrapolation maps to generate accurate estimates for the future trajectories of the qLPV scheduling parameters, as shown in recent findings. We show how these estimates can be used to enhance and fasten the corresponding MPC algorithms, offering comparable performances to state-of-the-art techniques, while maintaining relieved numerical burden during the implementation. Through realistic simulations of the Cartesian robot, we demonstrate the effectiveness and the real-time capabilities of the discussed method, which is tested against widely acknowledged techniques (the SQP qLPV MPC framework, and the CasADi NMPC solver).
Domaines
Automatique / RobotiqueOrigine | Fichiers produits par l'(les) auteur(s) |
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