Constrained LQR using online decomposition techniques

L. Ferranti, G Stathopoulos, C.N. Jones, T. Keviczky

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)


This paper presents an algorithm to solve the infinite horizon constrained linear quadratic regulator (CLQR) problem using operator splitting methods. First, the CLQR problem is reformulated as a (finite-time) model predictive control (MPC) problem without terminal constraints. Second, the MPC problem is decomposed into smaller subproblems of fixed dimension independent of the horizon length. Third, using the fast alternating minimization algorithm to solve the subproblems, the horizon length is estimated online, by adding or removing subproblems based on a periodic check on the state of the last subproblem to determine whether it belongs to a given control invariant set. We show that the estimated horizon length is bounded and that the control sequence computed using the proposed algorithm is an optimal solution of the CLQR problem. Compared to state-of-the-art algorithms proposed to solve the CLQR problem, our design solves at each iteration only unconstrained least-squares problems and simple gradient calculations. Furthermore, our technique allows the horizon length to decrease online. Numerical results on a planar system show the potential of our algorithm.

Original languageEnglish
Title of host publicationProceedings 2016 IEEE 55th Conference on Decision and Control (CDC)
EditorsFrancesco Bullo, Christophe Prieur, Alessandro Giua
Place of PublicationPiscataway, NJ, USA
ISBN (Electronic)978-1-5090-1837-6
Publication statusPublished - 2016
Event55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States
Duration: 12 Dec 201614 Dec 2016


Conference55th IEEE Conference on Decision and Control, CDC 2016
Abbreviated titleCDC 2016
CountryUnited States
CityLas Vegas


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