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

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

Zhang, Xi (Student TU Delft)
Ferranti, L. (TU Delft Team Tamas Keviczky)
Keviczky, T. (TU Delft Team Tamas Keviczky)

Astolfi, A (editor)

2017

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

http://resolver.tudelft.nl/uuid:843f5856-a932-489b-a91b-8916e8a5eb79

https://doi.org/10.1109/CDC.2017.8263807

IEEE, Piscataway, NJ, USA

978-1-5090-2874-0

Proceedings of the 2017 56th IEEE Annual Conference on Decision and Control

CDC 2017: 56th IEEE Annual Conference on Decision and Control, 2017-12-12 → 2017-12-15, Melbourne, Australia

Institutional Repository

conference paper

© 2017 Xi Zhang, L. Ferranti, T. Keviczky