ACADO

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 74 articles , 1 standard article )

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  1. Burk, Daniel; Völz, Andreas; Graichen, Knut: A modular framework for distributed model predictive control of nonlinear continuous-time systems (GRAMPC-D) (2022)
  2. Verschueren, Robin; Frison, Gianluca; Kouzoupis, Dimitris; Frey, Jonathan; van Duijkeren, Niels; Zanelli, Andrea; Novoselnik, Branimir; Albin, Thivaharan; Quirynen, Rien; Diehl, Moritz: \textttacados-- a modular open-source framework for fast embedded optimal control (2022)
  3. Arvind U. Raghunathan, Devesh K. Jha, Diego Romeres: PYROBOCOP : Python-based Robotic Control & Optimization Package for Manipulation and Collision Avoidance (2021) arXiv
  4. Faulwasser, Timm; Kellett, Christopher M.: On continuous-time infinite horizon optimal control -- dissipativity, stability, and transversality (2021)
  5. Fu, Jun; Tian, Fangyin: Dynamic optimization of nonlinear systems with guaranteed feasibility of inequality-path-constraints (2021)
  6. Gabiccini, M.; Bartali, L.; Guiggiani, M.: Analysis of driving styles of a GP2 car via minimum lap-time direct trajectory optimization (2021)
  7. Hespanhol, Pedro; Quirynen, Rien: Adjoint-based SQP method with block-wise quasi-Newton Jacobian updates for nonlinear optimal control (2021)
  8. Michałek, Maciej Marcin; Pazderski, Dariusz: Reconstruction of admissible joint-references from a prescribed output-reference for the non-standard and generalized N-trailers (2021)
  9. 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)
  10. Bastos, Guaraci jun.; Brüls, Olivier: Analysis of open-loop control design and parallel computation for underactuated manipulators (2020)
  11. Chen-Charpentier, Benito M.; Jackson, Mark: Direct and indirect optimal control applied to plant virus propagation with seasonality and delays (2020)
  12. Feng, Xuhui; Villanueva, Mario E.; Houska, Boris: Backward-forward reachable set splitting for state-constrained differential games (2020)
  13. Gros, Sébastien; Zanon, Mario; Quirynen, Rien; Bemporad, Alberto; Diehl, Moritz: From linear to nonlinear MPC: bridging the gap via the real-time iteration (2020)
  14. Gutekunst, Jürgen; Bock, Hans Georg; Potschka, Andreas: Economic NMPC for averaged infinite horizon problems with periodic approximations (2020)
  15. Liao-McPherson, Dominic; Nicotra, Marco M.; Kolmanovsky, Ilya: Time-distributed optimization for real-time model predictive control: stability, robustness, and constraint satisfaction (2020)
  16. Niu, Teng; Zhai, Jingang; Yin, Hongchao; Feng, Enmin; Liu, Chongyang; Xiu, Zhilong: Multi-objective optimisation of nonlinear switched systems in uncoupled fed-batch fermentation (2020)
  17. Xiaowei Xing, Dong Eui Chang: The Adaptive Dynamic Programming Toolbox (2020) arXiv
  18. Zanelli, A.; Domahidi, A.; Jerez, J.; Morari, M.: FORCES NLP: an efficient implementation of interior-point methods for multistage nonlinear nonconvex programs (2020)
  19. Zohdi, T. I.: \textitThegame of drones: rapid agent-based machine-learning models for multi-UAV path planning (2020)
  20. Abdollahpouri, Mohammad; Quirynen, Rien; Haring, Mark; Johansen, Tor Arne; Takács, Gergely; Diehl, Moritz; Rohaľ-Ilkiv, Boris: A homotopy-based moving horizon estimation (2019)

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