Benchmarking Nonlinear Model Predictive Control with Input Parameterizations - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2022

Benchmarking Nonlinear Model Predictive Control with Input Parameterizations

Guillaume Allibert
Olivier Kermorgant

Abstract

Model Predictive Control (MPC) while being a very effective control technique can become computationally demanding when a large prediction horizon is selected. To make the problem more tractable, one technique that has been proposed in the literature makes use of control input parameterizations to decrease the numerical complexity of nonlinear MPC problems without necessarily affecting the performances significantly. In this paper, we review the use of parameterizations and propose a simple Sequential Quadratic Programming algorithm for nonlinear MPC. We benchmark the performances of the solver in simulation and compare them with state-of-the-art solvers. Results show that parameterizations allow to attain good performances with (significantly) lower computation times.
Fichier principal
Vignette du fichier
MMAR22.pdf (2.59 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03701390 , version 1 (22-06-2022)
hal-03701390 , version 2 (30-06-2022)

Identifiers

  • HAL Id : hal-03701390 , version 2

Cite

Franco Fusco, Guillaume Allibert, Olivier Kermorgant, Philippe Martinet. Benchmarking Nonlinear Model Predictive Control with Input Parameterizations. International Conference on Methods and Models in Automation and Robotics, Aug 2022, Miedzyzdroje, Poland. ⟨hal-03701390v2⟩
95 View
369 Download

Share

Gmail Mastodon Facebook X LinkedIn More