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. (Source:

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 18 articles )

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  1. Aykutlug, Erkut; Topcu, Ufuk; Mease, Kenneth D.: Manifold-following approximate solution of completely hypersensitive optimal control problems (2016)
  2. Betts, John T.; Campbell, Stephen L.; Thompson, Karmethia C.: Solving optimal control problems with control delays using direct transcription (2016)
  3. 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)
  4. Choi, Han-Lim; Ha, Jung-Su: Informative windowed forecasting of continuous-time linear systems for mutual information-based sensor planning (2015)
  5. Kamalapurkar, Rushikesh; Dinh, Huyen; Bhasin, Shubhendu; Dixon, Warren E.: Approximate optimal trajectory tracking for continuous-time nonlinear systems (2015)
  6. Pahlajani, Chetan D.; Sun, Jianxin; Poulakakis, Ioannis; Tanner, Herbert G.: Error probability bounds for nuclear detection: improving accuracy through controlled mobility (2014)
  7. Peng, Hai-Jun; Gao, Qiang; Zhang, Hong-Wu; Wu, Zhi-Gang; Zhong, Wan-xie: Parametric variational solution of linear-quadratic optimal control problems with control inequality constraints (2014)
  8. Zhang, S.; Tang, G.J.; Friswell, M.I.; Wagg, D.J.: Multi-objective optimization of zero propellant spacecraft attitude maneuvers (2014)
  9. Elgindy, Kareem T.; Smith-Miles, Kate A.: Fast, accurate, and small-scale direct trajectory optimization using a Gegenbauer transcription method (2013)
  10. Li, Jie; An, Honglei; Zhu, Huayong; Shen, Lincheng; Fang, Bin: Geometric pseudospectral method on $SE(3)$ for rigid-body dynamics with application to aircraft (2013)
  11. Patterson, Michael A.; Weinstein, Matthew; Rao, Anil V.: An efficient overloaded method for computing derivatives of mathematical functions in MATLAB (2013)
  12. Lantoine, Gregory; Russell, Ryan P.: A hybrid differential dynamic programming algorithm for constrained optimal control problems. I: Theory (2012)
  13. Liu, Hongfu; Chen, Shaofei; Shen, Lincheng; Chen, Jing: An integrated multicriterion $hp$-adaptive pseudospectral method for direct optimal control problems solving (2012)
  14. Darby, Christopher L.; Hager, William W.; Rao, Anil V.: An $hp$-adaptive pseudospectral method for solving optimal control problems (2011)
  15. Garg, Divya; Hager, William W.; Rao, Anil V.: Pseudospectral methods for solving infinite-horizon optimal control problems (2011)
  16. Paoletti, Paolo; Genesio, Roberto: Rate limited time optimal control of a planar pendulum (2011)
  17. Wang, Xuezhong; Hsiang, Simon M.: Modeling trade-off between time-optimal and minimum energy in saccade main sequence (2011)
  18. Garg, Divya; Patterson, Michael; Hager, William W.; Rao, Anil V.; Benson, David A.; Huntington, Geoffrey T.: A unified framework for the numerical solution of optimal control problems using pseudospectral methods (2010)