GPOPS

GPOPS-II - MATLAB Optimal Control Software. GPOPS-II is the next-generation of general purpose optimal control software. GPOPS-II is a new MATLAB software intended to solve general nonlinear optimal control problems (that is, problems where it is desired to optimize systems defined by differential-algebraic equations). GPOPS-II implements the new class of variable-order Gaussian quadrature methods. Using GPOPS-II, the continuous-time optimal control problem is transcribed to a nonlinear programming problem (NLP). The NLP is then solved using either the NLP solver SNOPT or the NLP solver IPOPT. GPOPS-II has been written by Michael A. Patterson and Anil V. Rao and represents a major advancement in the numerical solution of optimal control problems. GPOPS-II is available at NO CHARGE TO MEMBERS OF THE UNIVERSITY OF FLORIDA OR ANY U.S. FEDERAL GOVERNMENT INSTITUTION. All others are required to pay a licensing fee for using GPOPS-II. See also: Algorithm 902: GPOPS, A MATLAB software for solving multiple-phase optimal control problems using the gauss pseudospectral method. (Source: http://freecode.com/)

This software is also peer reviewed by journal TOMS.


References in zbMATH (referenced in 50 articles , 2 standard articles )

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  1. Ha, Jung-Su; Choi, Han-Lim: On periodic optimal solutions of persistent sensor planning for continuous-time linear systems (2019)
  2. Wang, Pengling; Goverde, Rob M. P.: Multi-train trajectory optimization for energy-efficient timetabling (2019)
  3. Williams, Nakeya D.; Mehlsen, Jesper; Tran, Hien T.; Olufsen, Mette S.: An optimal control approach for blood pressure regulation during head-up tilt (2019)
  4. Campo-Duarte, Doris E.; Vasilieva, Olga; Cardona-Salgado, Daiver; Svinin, Mikhail: Optimal control approach for establishing wMelpop Wolbachia infection among wild Aedes aegypti populations (2018)
  5. Elgindy, Kareem T.: Optimal control of a parabolic distributed parameter system using a fully exponentially convergent barycentric shifted Gegenbauer integral pseudospectral method (2018)
  6. Jason K. Moore; Antonie van den Bogert: opty: Software for trajectory optimization and parameter identification using direct collocation (2018) not zbMATH
  7. 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)
  8. Putkaradze, Vakhtang; Rogers, Stuart: Constraint control of nonholonomic mechanical systems (2018)
  9. Wang, Hsuan-Hao; Lo, Yi-Su; Hwang, Feng-Tai; Hwang, Feng-Nan: A full-space quasi-Lagrange-Newton-Krylov algorithm for trajectory optimization problems (2018)
  10. Xiao, Long; Liu, Xinggao; Ma, Liang; Zhang, Zeyin: An effective pseudospectral method for constraint dynamic optimisation problems with characteristic times (2018)
  11. Frego, Marco; Bertolazzi, Enrico; Biral, Francesco; Fontanelli, Daniele; Palopoli, Luigi: Semi-analytical minimum time solutions with velocity constraints for trajectory following of vehicles (2017)
  12. Kelly, Matthew: An introduction to trajectory optimization: how to do your own direct collocation (2017)
  13. Mall, Kshitij; Grant, Michael James: Epsilon-Trig regularization method for bang-bang optimal control problems (2017)
  14. Quirynen, Rien; Gros, Sébastien; Houska, Boris; Diehl, Moritz: Lifted collocation integrators for direct optimal control in ACADO toolkit (2017)
  15. Scheepmaker, Gerben M.; Goverde, Rob M. P.; Kroon, Leo G.: Review of energy-efficient train control and timetabling (2017)
  16. Aykutlug, Erkut; Topcu, Ufuk; Mease, Kenneth D.: Manifold-following approximate solution of completely hypersensitive optimal control problems (2016)
  17. Betts, John T.; Campbell, Stephen L.; Thompson, Karmethia C.: Solving optimal control problems with control delays using direct transcription (2016)
  18. Campbell, Stephen; Kunkel, Peter: Solving higher index DAE optimal control problems (2016)
  19. Campbell, Stephen L.; Betts, John T.: Comments on direct transcription solution of DAE constrained optimal control problems with two discretization approaches (2016)
  20. Cannataro, Begüm Şenses; Rao, Anil V.; Davis, Timothy A.: State-defect constraint pairing graph coarsening method for Karush-Kuhn-Tucker matrices arising in orthogonal collocation methods for optimal control (2016)

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Further publications can be found at: http://www.gpops2.com/Publications/Publications.html