Low-complexity first-order constraint linearization methods for efficient nonlinear MPC

Conference paper (2017)

Authors

Giampaolo Torrisi ETH Zürich
S. Grammatico Team Bart De Schutter - Mechanical, Maritime and Materials Engineering
Damian Frick ETH Zürich
Tommaso Robbiani ETH Zürich
Roy S. Smith ETH Zürich
Manfred Morari University of Pennsylvania
Research Group
Team Bart De Schutter (Mechanical, Maritime and Materials Engineering) (TU Delft)
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Published Date

2017

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Bart De Schutter

Abstract

In this paper, we analyze first-order methods to find a KKT point of the nonlinear optimization problems arising in Model Predictive Control (MPC). The methods are based on a projected gradient and constraint linearization approach, that is, every iteration is a gradient step, projected onto a linearization of the constraints around the current iterate. We introduce an approach that uses a simple ℓp merit function, which has the computational advantage of not requiring any estimate of the dual variables and keeping the penalty parameter bounded. We then prove global convergence of the proposed method to a KKT point of the nonlinear problem. The first-order methods can be readily implemented in practice via the novel tool FalcOpt. The performance is then illustrated on numerical examples and compared with conventional methods.

Files

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