Optimica

Optimica — An Extension of Modelica Supporting Dynamic Optimization. In this paper, an extension of Modelica, entitled Optimica, is presented. Optimica extends Modelica with language constructs that enable formulation of dynamic optimization problems based on Modelica models. There are several important design problems that can be addressed by means of dynamic optimization, in a wide range of domains. Examples include, minimum-time problems, parameter estimation problems, and on-line optimization control strategies. The Optimica extension is supported by a prototype compiler, the Optimica compiler, which has been used successfully in case studies


References in zbMATH (referenced in 13 articles )

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  1. Arvind U. Raghunathan, Devesh K. Jha, Diego Romeres: PYROBOCOP : Python-based Robotic Control & Optimization Package for Manipulation and Collision Avoidance (2021) arXiv
  2. Elsheikh, Atiyah; Wiechert, Wolfgang: The structural index of sensitivity equation systems (2018)
  3. Magnusson, Fredrik; Åkesson, Johan: Symbolic elimination in dynamic optimization based on block-triangular ordering (2018)
  4. 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)
  5. Maree, Johannes Philippus; Imsland, Lars: Combined economic and regulatory predictive control (2016)
  6. Andersson, C., Führer, C., Åkesson, J.: Assimulo: A unified framework for ODE solvers (2015) not zbMATH
  7. Andersson, Christian; Führer, Claus; Åkesson, Johan: Assimulo: a unified framework for ODE solvers (2015)
  8. Elsheikh, Atiyah: An equation-based algorithmic differentiation technique for differential algebraic equations (2015)
  9. Limebeer, D. J. N.; Perantoni, G.; Rao, A. V.: Optimal control of formula one car energy recovery systems (2014)
  10. Patterson, Michael A.; Rao, Anil V.: (\mathbbGPOPS-\mathbbII): a MATLAB software for solving multiple-phase optimal control problems using (hp)-adaptive Gaussian quadrature collocation methods and sparse nonlinear programming (2014)
  11. Pytlak, Radosław; Tarnawski, Tomasz; Fajdek, Bartłomiej; Stachura, Marcin: Interactive dynamic optimization server -- connecting one modelling language with many solvers (2014)
  12. Word, Daniel P.; Kang, Jia; Akesson, Johan; Laird, Carl D.: Efficient parallel solution of large-scale nonlinear dynamic optimization problems (2014)
  13. Kirches, Christian; Leyffer, Sven: TACO: a toolkit for AMPL control optimization (2013)