Tutorial: Implementando Controladores Preditivos Não Lineares através do Ferramental LPV

  • Marcelo M. Morato Dept. de Automação e Sistemas (DAS), Univ. Fed. de Santa Catarina, Florianópolis-SC / Univ. Grenoble Alpes, CNRS, Grenoble INP (Institute of Engineering Univ. Grenoble Alpes), GIPSA-lab, Grenoble
  • Amir Naspolini Dept. de Automação e Sistemas (DAS), Univ. Fed. de Santa Catarina, Florianópolis-SC
  • Julio E. Normey-Rico Dept. de Automação e Sistemas (DAS), Univ. Fed. de Santa Catarina, Florianópolis-SC
Keywords: Model predictive control, LPV systems, Gas-lift

Abstract

Recent works have demonstrated how Linear Parameter Varying Model Predictive Control (LPV MPC) algorithms are able to control nonlinear systems with precision and reduced computational load. Specifically, these schemes achieve comparable performances to state-of-theart nonlinear MPCs, while requiring the solution of only one quadratic programming problem (thus being real-time capable). In this tutorial paper, we provide a step-by-step overview of how to implement such LPV MPC algorithms, covering from modelling to prediction aspects. For illustration purposes, we consider a realistic implementation for a gas-lift petroleoum extraction process, comparing the LPV approach with the becnhmark nonlinear MPC software CasADi.
Published
2023-10-18