ACADO Toolkit is a software environment and algorithm collection for automatic control and dynamic optimization. It provides a general framework for using a great variety of algorithms for direct optimal control, including model predictive control, state and parameter estimation and robust optimization. ACADO Toolkit is implemented as self-contained C++ code and comes along with user-friendly MATLAB interface. The object-oriented design allows for convenient coupling of existing optimization packages and for extending it with user-written optimization routines.

References in zbMATH (referenced in 64 articles , 1 standard article )

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  1. Arvind U. Raghunathan, Devesh K. Jha, Diego Romeres: PYROBOCOP : Python-based Robotic Control & Optimization Package for Manipulation and Collision Avoidance (2021) arXiv
  2. Fu, Jun; Tian, Fangyin: Dynamic optimization of nonlinear systems with guaranteed feasibility of inequality-path-constraints (2021)
  3. Michałek, Maciej Marcin; Pazderski, Dariusz: Reconstruction of admissible joint-references from a prescribed output-reference for the non-standard and generalized N-trailers (2021)
  4. Agamawi, Yunus M.; Rao, Anil V.: CGPOPS: a C++ software for solving multiple-phase optimal control problems using adaptive Gaussian quadrature collocation and sparse nonlinear programming (2020)
  5. Bastos, Guaraci jun.; Brüls, Olivier: Analysis of open-loop control design and parallel computation for underactuated manipulators (2020)
  6. Chen-Charpentier, Benito M.; Jackson, Mark: Direct and indirect optimal control applied to plant virus propagation with seasonality and delays (2020)
  7. Feng, Xuhui; Villanueva, Mario E.; Houska, Boris: Backward-forward reachable set splitting for state-constrained differential games (2020)
  8. Gros, Sébastien; Zanon, Mario; Quirynen, Rien; Bemporad, Alberto; Diehl, Moritz: From linear to nonlinear MPC: bridging the gap via the real-time iteration (2020)
  9. Gutekunst, Jürgen; Bock, Hans Georg; Potschka, Andreas: Economic NMPC for averaged infinite horizon problems with periodic approximations (2020)
  10. Liao-McPherson, Dominic; Nicotra, Marco M.; Kolmanovsky, Ilya: Time-distributed optimization for real-time model predictive control: stability, robustness, and constraint satisfaction (2020)
  11. Xiaowei Xing, Dong Eui Chang: The Adaptive Dynamic Programming Toolbox (2020) arXiv
  12. Abdollahpouri, Mohammad; Quirynen, Rien; Haring, Mark; Johansen, Tor Arne; Takács, Gergely; Diehl, Moritz; Rohaľ-Ilkiv, Boris: A homotopy-based moving horizon estimation (2019)
  13. Deng, Haoyang; Ohtsuka, Toshiyuki: A parallel Newton-type method for nonlinear model predictive control (2019)
  14. Englert, Tobias; Völz, Andreas; Mesmer, Felix; Rhein, Sönke; Graichen, Knut: A software framework for embedded nonlinear model predictive control using a gradient-based augmented Lagrangian approach (GRAMPC) (2019)
  15. Francesco Farina, Andrea Camisa, Andrea Testa, Ivano Notarnicola, Giuseppe Notarstefano: DISROPT: a Python Framework for Distributed Optimization (2019) arXiv
  16. Houska, Boris; Chachuat, Benoît: Global optimization in Hilbert space (2019)
  17. Loppi, N. A.; Witherden, F. D.; Jameson, A.; Vincent, P. E.: Locally adaptive pseudo-time stepping for high-order flux reconstruction (2019)
  18. Olsen, Christian Haargaard; Ottesen, Johnny T.; Smith, Ralph C.; Olufsen, Mette S.: Parameter subset selection techniques for problems in mathematical biology (2019)
  19. 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
  20. Buşoniu, Lucian; Páll, Előd; Munos, Rémi: Continuous-action planning for discounted infinite-horizon nonlinear optimal control with Lipschitz values (2018)

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