Draft:Acados

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acados
DeveloperSystems Control and Optimization Laboratory (research team of Prof. Moritz Diehl at the University of Freiburg)
Initial releaseAugust 27, 2019; 6 years ago (2019-08-27)
Stable release
0.5.4[1] Edit this on Wikidata / 4 May 2026
Written inC with interfaces to Python, GNU Octave, MATLAB, Simulink
Operating systemLinux, Windows and macOS
TypeNonlinear optimal control and mathematical optimization
License 2-clause BSD license (free software)
Websitedocs.acados.org

acados is a free and open source software framework for nonlinear model predictive control and moving horizon estimation[2][3][4]. It implements the sequential quadratic programming method, and it relies on existing open-source solvers to solve the underlying quadratic programs, like HPIPM (Q134395572) or qpOASES (Q134395611). It uses CasADi symbolic framework to define the problem equations and to compute their derivatives through automatic differentiation. The library acados and its dependencies are designed to target problems arising in optimal control and trajectory optimization which have a specific structure. The software can be used directly from C or from its higher-level interface where the user can define the problem in Python or Matlab and an equivalent problem specification in C is automatically generated via a Template processor. It was influenced by ACADO Toolkit (Q134452070), from which the name is inspired. While ACADO is a code-generation tool, acados uses code-generation only for derivatives and interfaces and is thus optional.

There are records of its adoption mostly in academia. It has been used on range of different embedded control applications like wheeled vehicles[5], autonomous drones[6][7][8], autonomous water taxis[9], legged locomotion[10], electric motor[11] and wind turbine control[12]. A notable use in industry is by the semi-automated driving startup comma.ai which uses acados inside its product openpilot.[13]

See also

References

  1. ^ "Release 0.5.4". 4 May 2026. Retrieved 5 May 2026.
  2. ^ Robin Verschueren; Gianluca Frison; Dimitris Kouzoupis; et al. (9 October 2021). "acados—a modular open-source framework for fast embedded optimal control". Mathematical Programming Computation. 14 (1): 147–183. arXiv:1910.13753. doi:10.1007/S12532-021-00208-8. ISSN 1867-2957. Wikidata Q115144371.
  3. ^ Johannes Köhler; Matthias A. Müller; Frank Allgöwer (2024). "Analysis and design of model predictive control frameworks for dynamic operation—An overview". Annual Reviews in Control. 57: 100929. doi:10.1016/J.ARCONTROL.2023.100929. ISSN 1367-5788. Wikidata Q134301032.{{cite journal}}: CS1 maint: article number as page number (link)
  4. ^ Maximilian Schaller; Goran Banjac; Steven Diamond; Akshay Agrawal; Bartolomeo Stellato; Stephen Boyd (2022). "Embedded Code Generation With CVXPY" (PDF). IEEE control systems letters. 6: 2653–2658. doi:10.1109/LCSYS.2022.3173209. ISSN 2475-1456. Wikidata Q120716403.
  5. ^ P. Stano; U. Montanaro; D. Tavernini; M. Tufo; G. Fiengo; L. Novella; A. Sorniotti (7 December 2022). "Model predictive path tracking control for automated road vehicles: A review". Annual Reviews in Control. 55: 194–236. doi:10.1016/J.ARCONTROL.2022.11.001. ISSN 1367-5788. Wikidata Q134445601.
  6. ^ Angel Romero; Robert Penicka; Davide Scaramuzza (23 June 2022). "Time-Optimal Online Replanning for Agile Quadrotor Flight". IEEE robotics and automation letters. doi:10.1109/LRA.2022.3185772. ISSN 2377-3766. Wikidata Q134433262.
  7. ^ Tim Salzmann; Elia Kaufmann; Jon Arrizabalaga; Marco Pavone; Davide Scaramuzza; Markus Ryll (20 February 2023). "Real-Time Neural MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms" (PDF). IEEE robotics and automation letters. doi:10.1109/LRA.2023.3246839. ISSN 2377-3766. Wikidata Q134433254.
  8. ^ Edison Velasco-Sánchez; Luis F. Recalde; Bryan S. Guevara; José Varela-Aldás; Francisco A. Candelas; Santiago T. Puente; Daniel C. Gandolfo (March 2024). "Visual Servoing NMPC Applied to UAVs for Photovoltaic Array Inspection". IEEE robotics and automation letters. 9 (3): 2766–2773. arXiv:2311.08019. doi:10.1109/LRA.2024.3360876. ISSN 2377-3766. Wikidata Q128964934.
  9. ^ Hannes Homburger; Stefan Wirtensohn; Patrick Hoher; Tim Baur; Dennis Griesser; Moritz Diehl; Johannes Reuter (June 2025). "Solgenia—A test vessel toward energy-efficient autonomous water taxi applications". Ocean Engineering (in French). 328: 121011. doi:10.1016/J.OCEANENG.2025.121011. ISSN 0029-8018. Wikidata Q134445571.{{cite journal}}: CS1 maint: article number as page number (link)
  10. ^ Niraj Rathod; Angelo Bratta; Michele Focchi; Mario Zanon; Octavio Villarreal; Claudio Semini; Alberto Bemporad (2021). "Model Predictive Control With Environment Adaptation for Legged Locomotion". IEEE Access. 9: 145710–145727. doi:10.1109/ACCESS.2021.3118957. ISSN 2169-3536. Wikidata Q134445583.
  11. ^ Andrea Zanelli; Julian Kullick; Hisham M. Eldeeb; Gianluca Frison; Christoph M. Hackl; Moritz Diehl (22 February 2021). "Continuous Control Set Nonlinear Model Predictive Control of Reluctance Synchronous Machines". IEEE Transactions on Control Systems Technology. doi:10.1109/TCST.2020.3043956. ISSN 1063-6536. Wikidata Q134433264.
  12. ^ Stefan Loew; Carlo L Bottasso (3 August 2022). "Lidar-assisted model predictive control of wind turbine fatigue via online rainflow counting considering stress history". Wind Energy Science. 7 (4): 1605–1625. doi:10.5194/WES-7-1605-2022. ISSN 2366-7443. Wikidata Q114571268.
  13. ^ "Openpilot0.8.10". November 2021.

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