A global piecewise smooth Newton method for fast large-scale model predictive control

Authors:
Panagiotis Patrinos;Pantelis Sopasakis;Haralambos Sarimveis
Affiliations:
Department of Mechanical and Structural Engineering, University of Trento, Via Mesiano 77, 38100 Trento, Italy;School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechneiou Street, 15780 Zografou Campus, Athens, Greece;School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechneiou Street, 15780 Zografou Campus, Athens, Greece
Venue:
Automatica (Journal of IFAC)
Year:
2011

Citing 9
Cited 0

Finite algorithms for robust linear regression

BIT
On the linear convergence of descent methods for convex essentially smooth minimization

SIAM Journal on Control and Optimization
Error Bounds for Piecewise Convex Quadratic Programs and Applications

SIAM Journal on Control and Optimization
Application of interior-point methods to model predictive control

Journal of Optimization Theory and Applications
A New Algorithm for Solving Strictly Convex Quadratic Programs

SIAM Journal on Optimization
Brief paper: Fast, large-scale model predictive control by partial enumeration

Automatica (Journal of IFAC)
Survey Constrained model predictive control: Stability and optimality

Automatica (Journal of IFAC)
Brief Equivalence of hybrid dynamical models

Automatica (Journal of IFAC)
The explicit linear quadratic regulator for constrained systems

Automatica (Journal of IFAC)

Quantified Score

Hi-index	22.14

Visualization

Abstract

In this paper, the strictly convex quadratic program (QP) arising in model predictive control (MPC) for constrained linear systems is reformulated as a system of piecewise affine equations. A regularized piecewise smooth Newton method with exact line search on a convex, differentiable, piecewise-quadratic merit function is proposed for the solution of the reformulated problem. The algorithm has considerable merits when applied to MPC over standard active set or interior point algorithms. Its performance is tested and compared against state-of-the-art QP solvers on a series of benchmark problems. The proposed algorithm is orders of magnitudes faster, especially for large-scale problems and long horizons. For example, for the challenging crude distillation unit model of Pannocchia, Rawlings, and Wright (2007) with 252 states, 32 inputs, and 90 outputs, the average running time of the proposed approach is 1.57 ms.