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:

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 81 articles , 2 standard articles )

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  1. Febrer-Nafría, Míriam; Pallarès-López, Roger; Fregly, Benjamin J.; Font-Llagunes, Josep M.: Prediction of three-dimensional crutch walking patterns using a torque-driven model (2021)
  2. Robert Falck; Justin S. Gray; Kaushik Ponnapalli; Ted Wright: dymos: A Python package for optimal control of multidisciplinary systems (2021) not zbMATH
  3. 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)
  4. Arias-Castro, Juddy Heliana; Martinez-Romero, Hector Jairo; Vasilieva, Olga: Biological and chemical control of mosquito population by optimal control approach (2020)
  5. Berret, Bastien; Jean, Frédéric: Efficient computation of optimal open-loop controls for stochastic systems (2020)
  6. Betts, John T.; Campbell, Stephen L.; Digirolamo, Claire: Initial guess sensitivity in computational optimal control problems (2020)
  7. Brown, Colin; McNally, William; McPhee, John: Optimal control of joint torques using direct collocation to maximize ball carry distance in a golf swing (2020)
  8. Cardona-Salgado, Daiver; Campo-Duarte, Doris E.; Sepulveda-Salcedo, Lilian S.; Vasilieva, Olga: \textitWolbachia-based biocontrol for dengue reduction using dynamic optimization approach (2020)
  9. Inkol, Keaton A.; Brown, Colin; McNally, William; Jansen, Conor; McPhee, John: Muscle torque generators in multibody dynamic simulations of optimal sports performance (2020)
  10. Jansen, Conor; McPhee, John: Predictive dynamic simulation of olympic track cycling standing start using direct collocation optimal control (2020)
  11. Ledzewicz, Urszula; Schättler, Heinz: On the role of the objective in the optimization of compartmental models for biomedical therapies (2020)
  12. Xiaowei Xing, Dong Eui Chang: The Adaptive Dynamic Programming Toolbox (2020) arXiv
  13. Zhao, Jisong; Li, Shuang: Adaptive mesh refinement method for solving optimal control problems using interpolation error analysis and improved data compression (2020)
  14. Campbell, Stephen; Kunkel, Peter: General nonlinear differential algebraic equations and tracking problems: a robotics example (2019)
  15. Hager, William W.; Hou, Hongyan; Mohapatra, Subhashree; Rao, Anil V.; Wang, Xiang-Sheng: Convergence rate for a Radau hp collocation method applied to constrained optimal control (2019)
  16. Ha, Jung-Su; Choi, Han-Lim: On periodic optimal solutions of persistent sensor planning for continuous-time linear systems (2019)
  17. Hu, Xiaochuan; Ke, Guoyi; Jang, Sophia R.-J.: Modeling pancreatic cancer dynamics with immunotherapy (2019)
  18. Pei, Pei; Wang, Jiang: Near-optimal guidance with impact angle and velocity constraints using sequential convex programming (2019)
  19. Wang, Pengling; Goverde, Rob M. P.: Multi-train trajectory optimization for energy-efficient timetabling (2019)
  20. Wan, Shizheng; Chang, Xiaofei; Li, Quancheng; Yan, Jie: Finite-horizon optimal tracking guidance for aircraft based on approximate dynamic programming (2019)

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Further publications can be found at: