An improved dual Newton strategy for scenario-tree MPC
Paper i proceeding, 2016

This paper considers the problem of solving Quadratic Programs (QPs) in the context of robust Model Predictive Control (MPC) based on scenario trees. A Newton strategy is used in conjunction with dual decomposition, yield- ing a parallelizable method with a fast practical convergence. In this context, it has been observed that the Hessian of the dual function has an intricate sparsity structure and can be rank deficient, hence requiring a computationally expensive linear algebra and a regularization strategy. In this paper, we show that it is possible to organize the robust MPC problem such that the dual Hessian has a block-tridiagonal structure, hence reducing dramatically the cost of its factorization. Moreover, a simple and inexpensive strategy of constraint elimination is pro- posed for ensuring the positive definiteness of the dual Hessian, making regularization superfluous. This strategy additionally allows for evening the computational burden of computing the robust MPC solution in its parallelization.


Emil Klintberg

Chalmers, Signaler och system, System- och reglerteknik

John Dahl

Volvo Cars

Jonas Fredriksson

Chalmers, Signaler och system, System- och reglerteknik

Sébastien Gros

Chalmers, Signaler och system, System- och reglerteknik

Proceedings of the 55th IEEE Conference on Decision and Control (CDC 2016); Las Vegas; United States; 12-14 December 2016

0743-1546 (ISSN)

Art no 7798822, Pages 3675-3681
978-1-5090-1837-6 (ISBN)




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