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A dual Newton strategy for the efficient solution of sparse quadratic programs arising in SQP-based nonlinear MPC

Janick Frasch (frasch***at***ovgu.de)
Milan Vukov (milan.vukov***at***esat.kuleuven.be)
Hans Joachim Ferreau (joachim.ferreau***at***ch.abb.com)
Moritz Diehl (moritz.diehl***at***esat.kuleuven.be)

Abstract: A large class of linear and nonlinear model predictive control (MPC) algorithms requires the solution of a sparse structured quadratic program (QP) at each sampling time. We propose a novel algorithm based on a dual Newton strategy that aims at combining sparsity exploitation features of an interior point method with warm-starting capabilities of an active-set method. We address algorithmic details and present the open-source implementation qpDUNES. The performance of the solver in combination with the ACADO Code Generation tool for nonlinear MPC is assessed based on two benchmark problems.

Keywords: Optimal Control, Quadratic Programming, Model Predictive Control, Open-Source Software

Category 1: Nonlinear Optimization (Quadratic Programming )

Category 2: Applications -- Science and Engineering (Control Applications )

Category 3: Optimization Software and Modeling Systems (Parallel Algorithms )

Citation: Internal report (to be submitted), KU Leuven, 02/2013

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Entry Submitted: 07/24/2013
Entry Accepted: 07/24/2013
Entry Last Modified: 12/08/2014

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