%0 Journal Article
%@ 0018-9286
%A Bemporad, Alberto
%D 2016
%F eprints:2787
%I IEEE
%J IEEE Transactions on Automatic Control
%K Linear systems;MATLAB;Matrix decomposition;Prediction algorithms;Predictive control;Quadratic programming;Active set methods;Model predictive control;Nonnegative least squares;Quadratic programming
%N 4
%P 1111-1116
%T A Quadratic Programming Algorithm Based on Nonnegative Least Squares with Applications to Embedded Model Predictive Control
%U http://eprints.imtlucca.it/2787/
%V 61
%X This paper proposes an active set method based on nonnegative least squares (NNLS) to solve strictly convex quadratic programming (QP) problems, such as those that arise in Model Predictive Control (MPC). The main idea is to rephrase the QP problem as a Least Distance Problem (LDP) that is solved via a NNLS reformulation. While the method is rather general for solving strictly convex QP’s subject to linear inequality constraints, it is particularly useful for embedded MPC because (i) is very fast, compared to other existing state-of-theart QP algorithms, (ii) is very simple to code, requiring only basic arithmetic operations for computing LDLT decompositions recursively to solve linear systems of equations, (iii) contrary to iterative methods, provides the solution or recognizes infeasibility in a finite number of steps.