Recent Advances in Quadratic Programming Algorithms for Nonlinear Model Predictive Control

Over the past decades, the advantages of optimization-based control techniques over conventional controllers inspired developments that enabled the use of model predictive control (MPC) in applications with very high sampling rates. Since at the heart of most linear and nonlinear MPC controllers resides a quadratic programming (QP) solver, the implementation of efficient algorithms that exploit the underlying problem structure drew the attention of many researchers and the progress in the field has been remarkable. The aim of this paper is to summarize the main algorithmic advances in the field and to provide a consistent benchmark between a selection of software tools that have been recently developed. The code that was used for the simulations is publicly available for readers that wish to reproduce the results or test the benchmarked solvers on their own nonlinear MPC applications.

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