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A Robust Nonlinear Tracking MPC using qLPV Embedding and Zonotopic Uncertainty Propagation
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  • Marcelo Menezes Morato,
  • Victor Cunha,
  • Tito L. M. Santos,
  • Júlio Normey-Rico,
  • Olivier sename
Marcelo Menezes Morato

Corresponding Author:marcelomnzm@gmail.com

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Victor Cunha
Universidade Federal da Bahia
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Tito L. M. Santos
Universidade Federal da Bahia
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Júlio Normey-Rico
Federal University of Santa Catarina
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Olivier sename
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In this paper, we propose a novel Nonlinear Model Predictive Control (NMPC) framework for tracking for piece-wise constant reference signals. The main novelty is the use quasi-Linear Parameter Varying (qLPV) embeddings in order to describe the nonlinear dynamics. Furthermore, these embeddings are exploited by an extrapo- lation mechanism, which provides the future behaviour of the scheduling parameters with bounded estimation error. Therefore, the resulting NMPC becomes compu- tationally efficient (comparable to a Quadratic Programming algorithm), since, at each sampling period, the predictions are linear. Benefiting from artificial target variables, the method is also able to avoid feasibility losses due to large set-point variations. Robust constraint satisfaction, closed-loop stability, and recursive fea- sibility certificates are provided, thanks to uncertainty propagation zonotopes and parameter-dependent terminal ingredients. A benchmark example is used to illustrate the effectiveness of the method, which is compared to state-of-the-art techniques.