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 59 articles , 1 standard article )

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  1. Bastos, Guaraci jun.; Brüls, Olivier: Analysis of open-loop control design and parallel computation for underactuated manipulators (2020)
  2. Chen-Charpentier, Benito M.; Jackson, Mark: Direct and indirect optimal control applied to plant virus propagation with seasonality and delays (2020)
  3. Feng, Xuhui; Villanueva, Mario E.; Houska, Boris: Backward-forward reachable set splitting for state-constrained differential games (2020)
  4. Gros, Sébastien; Zanon, Mario; Quirynen, Rien; Bemporad, Alberto; Diehl, Moritz: From linear to nonlinear MPC: bridging the gap via the real-time iteration (2020)
  5. Gutekunst, Jürgen; Bock, Hans Georg; Potschka, Andreas: Economic NMPC for averaged infinite horizon problems with periodic approximations (2020)
  6. Liao-McPherson, Dominic; Nicotra, Marco M.; Kolmanovsky, Ilya: Time-distributed optimization for real-time model predictive control: stability, robustness, and constraint satisfaction (2020)
  7. Abdollahpouri, Mohammad; Quirynen, Rien; Haring, Mark; Johansen, Tor Arne; Takács, Gergely; Diehl, Moritz; Rohaľ-Ilkiv, Boris: A homotopy-based moving horizon estimation (2019)
  8. Deng, Haoyang; Ohtsuka, Toshiyuki: A parallel Newton-type method for nonlinear model predictive control (2019)
  9. 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)
  10. Francesco Farina, Andrea Camisa, Andrea Testa, Ivano Notarnicola, Giuseppe Notarstefano: DISROPT: a Python Framework for Distributed Optimization (2019) arXiv
  11. Houska, Boris; Chachuat, Benoît: Global optimization in Hilbert space (2019)
  12. Olsen, Christian Haargaard; Ottesen, Johnny T.; Smith, Ralph C.; Olufsen, Mette S.: Parameter subset selection techniques for problems in mathematical biology (2019)
  13. 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
  14. Buşoniu, Lucian; Páll, Előd; Munos, Rémi: Continuous-action planning for discounted infinite-horizon nonlinear optimal control with Lipschitz values (2018)
  15. Gaitsgory, Vladimir; Grüne, Lars; Höger, Matthias; Kellett, Christopher M.; Weller, Steven R.: Stabilization of strictly dissipative discrete time systems with discounted optimal control (2018)
  16. Houska, Boris; Li, Jiaqi C.; Chachuat, Benoît: Towards rigorous robust optimal control via generalized high-order moment expansion (2018)
  17. Jiang, Canghua; Xie, Kun; Yu, Changjun; Yu, Ming; Wang, Hai; He, Yigang; Teo, Kok Lay: A sequential computational approach to optimal control problems for differential-algebraic systems based on efficient implicit Runge-Kutta integration (2018)
  18. Kayacan, Erkan; Kayacan, Erdal; Chen, I-Ming; Ramon, Herman; Saeys, Wouter: On the comparison of model-based and model-free controllers in guidance, navigation and control of agricultural vehicles (2018)
  19. Kouzoupis, Dimitris; Frison, Gianluca; Zanelli, Andrea; Diehl, Moritz: Recent advances in quadratic programming algorithms for nonlinear model predictive control (2018)
  20. Nicholson, Bethany; Siirola, John D.; Watson, Jean-Paul; Zavala, Victor M.; Biegler, Lorenz T.: \textttpyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations (2018)

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