An improved primal-dual interior-point solver for model predictive control

Conference Paper (2017)
Author(s)

Xi Zhang (Student TU Delft)

Laura Ferranti (TU Delft - Team Tamas Keviczky)

T. Keviczky (TU Delft - Team Tamas Keviczky)

Research Group
Team Tamas Keviczky
Copyright
© 2017 Xi Zhang, L. Ferranti, T. Keviczky
DOI related publication
https://doi.org/10.1109/CDC.2017.8263807
More Info
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Publication Year
2017
Language
English
Copyright
© 2017 Xi Zhang, L. Ferranti, T. Keviczky
Research Group
Team Tamas Keviczky
Pages (from-to)
1126-1131
ISBN (print)
978-1-5090-2874-0
ISBN (electronic)
978-1-5090-2873-3
Reuse Rights

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Abstract

We propose a primal-dual interior-point (PDIP) method for solving quadratic programming problems with linear inequality constraints that typically arise from MPC applications. We show that the solver converges (locally) quadratically to a suboptimal solution of the MPC problem. PDIP solvers rely on two phases: the damped and the pure Newton phases. Compared to state-of-the-art PDIP methods, our solver replaces the initial damped Newton phase (usually used to compute a medium-accuracy solution) with a dual solver based on Nesterov's fast gradient scheme (DFG) that converges with a sublinear convergence rate of order O(1/k2) to a medium-accuracy solution. The switching strategy to the pure Newton phase, compared to the state of the art, is computed in the dual space to exploit the dual information provided by the DFG in the first phase. Removing the damped Newton phase has the additional advantage that our solver saves the computational effort required by backtracking line search. The effectiveness of the proposed solver is demonstrated on a 2-dimensional discrete-time unstable system and on an aerospace application

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