Citing#

If you are using acados in your scientific work, please cite the original journal publication.

@Article{Verschueren2021,
  Title                    = {acados -- a modular open-source framework for fast embedded optimal control},
  Author                   = {Robin Verschueren and Gianluca Frison and Dimitris Kouzoupis and Jonathan Frey and Niels van Duijkeren and Andrea Zanelli and Branimir Novoselnik and Thivaharan Albin and Rien Quirynen and Moritz Diehl},
  Journal                  = {Mathematical Programming Computation},
  Year                     = {2021},
}

We highly appreciate if you share your acados success stories with the world, and mention them on the List of projects that feature acados. Please submit a PR, make a forum post, or contact the developers if you want to showcase your project there!

Publications on advanced `acados` features#

If you are using some of the following advanced features of acados, please additionally cite the corresponding publications.

Fast integrators with sensitivity propagation for use in CasADi#

This paper demonstrates the efficiency of acados integrators, compared with integrators available in CasADi. Additionally it describes the casados-integrator a wrapper which allows using the acados integrators within a CasADi NLP solver, like IPOPT.

@InProceedings{Frey2023,
  Title                    = {Fast integrators with sensitivity propagation for use in {C}as{AD}i},
  Author                   = {Frey, Jonathan and De Schutter, Jochem and Diehl, Moritz},
  Booktitle                = ECC,
  Year                     = {2023},
}

Multi-Phase Optimal Control Problems for Efficient Nonlinear Model Predictive Control with acados#

Computationally efficient nonlinear model predictive control relies on elaborate discrete-time optimal control problem (OCP) formulations trading off accuracy with respect to the continuous-time problem and associated computational burden. Such formulations, however, are in general not easy to implement within specialized software frameworks tailored to numerical optimal control. This paper introduces a new multi-phase OCP interface for the open-source software acados allowing to conveniently formulate such problems and generate fast solvers that can be used for nonlinear model predictive control (NMPC). While multi-phase OCP (MOCP) formulations occur naturally in many applications, this paper focuses on MOCP formulations that can be used to efficiently approximate standard continuous-time OCPs in the context of NMPC.

This feature can be used via the AcadosMultiphaseOcp class.

@misc{Frey2024MultiPhase,
      title={Multi-Phase Optimal Control Problems for Efficient Nonlinear Model Predictive Control with acados},
      author={Jonathan Frey and Katrin Baumgärtner and Gianluca Frison and Moritz Diehl},
      year={2024},
      eprint={2408.07382},
      archivePrefix={arXiv},
      primaryClass={math.OC},
      url={https://arxiv.org/abs/2408.07382},
}

Advanced-Step Real-Time Iterations (AS-RTI)#

Advanced-step real-time iterations provide an extension to the classic real-time iteration algorithm, which allows to performs additional multi-level iterations in the preparation phase, such as inexact or zero-order SQP iterations on a problem with a predicted state estimate.

This feature can be used by setting the options as_rti_level and as_rti_level.

@Misc{Frey2024a,
  Title                    = {Advanced-Step Real-Time Iterations with Four Levels -- New Error Bounds and Fast Implementation in acados},
  Author                   = {Jonathan Frey and Armin Nurkanovic and Moritz Diehl},
  Year                     = {2024},
  Eprint                   = {2403.07101},
  Primaryclass             = {math.OC},
  Url                      = {https://arxiv.org/abs/2403.07101}
}

Gauss-Newton Runge-Kutta (GNRK) integrators for efficient discretization of OCPs with long horizons and least-squares costs#

The GNRK integration scheme can be used by setting the option cost_discretization = 'INTEGRATOR'. This paper additionally demonstrates the effectiveness of using nonuniform discretization grids, and in particular in combination with GNRK.

@InProceedings{Frey2023b,
  Title                    = {{G}auss-{N}ewton {R}unge-{K}utta Integration for Efficient Discretization of Optimal Control Problems with Long Horizons and Least-Squares Costs},
  Author                   = {Jonathan Frey and Katrin Baumgärtner and Moritz Diehl},
  Booktitle                = {accepted for ECC 2024},
  Url                      = {https://arxiv.org/abs/2310.00618}
}

Efficient Zero-Order Robust Optimization (zoRO) for Real-Time Model Predictive Control with acados#

@InProceedings{Frey2024,
  Title                    = {Efficient Zero-Order Robust Optimization for Real-Time Model Predictive Control with acados},
  Author                   = {Jonathan Frey and Yunfan Gao and Florian Messerer and Amon Lahr and Melanie N Zeilinger and Moritz Diehl},
  Booktitle                = ECC,
  Year                     = {2024},
}

Structure exploiting implicit Runge-Kutta method: GNSF#

The GNSF IRK integrator be used by setting the option integrator_tpye = 'GNSF'.

@InProceedings{Frey2019,
  Title                    = {Detecting and Exploiting {G}eneralized {N}onlinear {S}tatic {F}eedback
Structures in {DAE} Systems for {MPC}},
  Author                   = {Jonathan Frey and Rien Quirynen and Dimitris Kouzoupis and Gianluca Frison and
Jens Geisler and Axel Schild and Moritz Diehl},
  Booktitle                = ECC,
  Year                     = {2019},
}

Citing

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