qpOASES – Online Active Set Strategy. qpOASES is an open-source C++ implementation of the recently proposed online active set strategy (see [Ferreau, 2006], [Ferreau et al., 2008]), which was inspired by important observations from the field of parametric quadratic programming. It has several theoretical features that make it particularly suited for model predictive control (MPC) applications. The software package qpOASES implements these ideas and has already been successfully used within industrial projects and, e.g., for closed-loop control of a real-world Diesel engine [Ferreau et al., 2007]. Recently, a couple of numerical modifications (as proposed in [Potschka et al., 2010]) have been implemented that greatly increase qpOASES’s reliability when solving semi-definite, ill-posed or degenerated convex QPs. (Source: http://plato.asu.edu)

References in zbMATH (referenced in 66 articles )

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  1. Otta, Pavel; Burant, Jiří; Šantin, Ondřej; Havlena, Vladimír: Newton projection with proportioning using iterative linear algebra for model predictive control with long prediction horizon (2019)
  2. Perne, Matija; Gerkšič, Samo; Pregelj, Boštjan: Soft inequality constraints in gradient method and fast gradient method for quadratic programming (2019)
  3. Robin Verschueren, Gianluca Frison, Dimitris Kouzoupis, Niels van Duijkeren, Andrea Zanelli, Branimir Novoselnik, Jonathan Frey, Thivaharan Albin, Rien Quirynen, Moritz Diehl: acados: a modular open-source framework for fast embedded optimal control (2019) arXiv
  4. Wang, Guoqiang; Yu, Bo: PAL-Hom method for QP and an application to LP (2019)
  5. Weber, Tobias; Sager, Sebastian; Gleixner, Ambros: Solving quadratic programs to high precision using scaled iterative refinement (2019)
  6. Huber, Andreas; Gerdts, Matthias; Bertolazzi, Enrico: Structure exploitation in an interior-point method for fully discretized, state constrained optimal control problems (2018)
  7. Korda, Milan; Mezić, Igor: Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control (2018)
  8. Kouzoupis, Dimitris; Frison, Gianluca; Zanelli, Andrea; Diehl, Moritz: Recent advances in quadratic programming algorithms for nonlinear model predictive control (2018)
  9. Nikolić, Milutin; Borovac, Branislav; Raković, Mirko: Dynamic balance preservation and prevention of sliding for humanoid robots in the presence of multiple spatial contacts (2018)
  10. Quirynen, Rien; Gros, Sébastien; Diehl, Moritz: Inexact Newton-type optimization with iterated sensitivities (2018)
  11. Koehler, Sarah; Danielson, Claus; Borrelli, Francesco: A primal-dual active-set method for distributed model predictive control (2017)
  12. Quirynen, Rien; Gros, Sébastien; Houska, Boris; Diehl, Moritz: Lifted collocation integrators for direct optimal control in ACADO toolkit (2017)
  13. Šantin, Ondřej; Jarošová, Marta; Havlena, Vladimír; Dostál, Zdeněk: Proportioning with second-order information for model predictive control (2017)
  14. Verschueren, Robin; Zanon, Mario; Quirynen, Rien; Diehl, Moritz: A sparsity preserving convexification procedure for indefinite quadratic programs arising in direct optimal control (2017)
  15. Zanon, Mario; Boccia, Andrea; Palma, Vryan Gil S.; Parenti, Sonja; Xausa, Ilaria: Direct optimal control and model predictive control (2017)
  16. Forsgren, Anders; Gill, Philip E.; Wong, Elizabeth: Primal and dual active-set methods for convex quadratic programming (2016)
  17. Janka, Dennis; Kirches, Christian; Sager, Sebastian; Wächter, Andreas: An SR1/BFGS SQP algorithm for nonconvex nonlinear programs with block-diagonal Hessian matrix (2016)
  18. Curtis, Frank E.; Han, Zheng; Robinson, Daniel P.: A globally convergent primal-dual active-set framework for large-scale convex quadratic optimization (2015)
  19. Frasch, Janick V.; Sager, Sebastian; Diehl, Moritz: A parallel quadratic programming method for dynamic optimization problems (2015)
  20. Gill, Philip E.; Wong, Elizabeth: Methods for convex and general quadratic programming (2015)