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Second class constraints and the consistency of optimal control theory in phase space

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  • Contreras G., Mauricio
  • Peña, Juan Pablo
  • Aros, Rodrigo

Abstract

As has been shown in the literature (Rothe and Rothe, 2010; Rothe and Scholtz, 2003), the description of a mechanical system in terms of canonical transformations together with the Hamilton–Jacobi equation for the S function is ill-defined when the system has second class constraints. In this case, Carathéodory’s integrability conditions are violated and either the corresponding Hamilton–Jacobi equation cannot be solved or their solutions do not describe the system at all. This can be remedied by enlarging the phase space so that the constraints become first class in the extended space. Another way to approach this problem, is to apply the Rothe–Scholtz method discussed in Rothe and Rothe (2010) and Rothe and Scholtz (2003), so that the constraints themselves become variables of a new canonical transformation. This method works when the elements of the Dirac matrix are constant. On the other hand, it has been shown that optimal control theory can be written in phase space as a mechanical system with second class restrictions (Itami, 2001; Hojman, 0000; Contreras et al., 2017; Contreras and Peña 2018). This implies that the description of control theory can become inconsistent in terms of the Hamilton–Jacobi equation. In this article we will use the description of Rothe–Scholtz to analyse a subclass of LQ linear-quadratic problems whose Dirac matrix is constant and to check if the integrability conditions can be fulfilled so as to not get inconsistencies.

Suggested Citation

  • Contreras G., Mauricio & Peña, Juan Pablo & Aros, Rodrigo, 2021. "Second class constraints and the consistency of optimal control theory in phase space," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
  • Handle: RePEc:eee:phsmap:v:562:y:2021:i:c:s0378437120307032
    DOI: 10.1016/j.physa.2020.125335
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    References listed on IDEAS

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    1. Caputo,Michael R., 2005. "Foundations of Dynamic Economic Analysis," Cambridge Books, Cambridge University Press, number 9780521842723.
    2. Richard Bellman, 1954. "Some Applications of the Theory of Dynamic Programming---A Review," Operations Research, INFORMS, vol. 2(3), pages 275-288, August.
    3. Contreras, Mauricio & Pellicer, Rely & Villena, Marcelo, 2017. "Dynamic optimization and its relation to classical and quantum constrained systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 12-25.
    4. Richard Bellman, 1954. "On some applications of the theory of dynamic programming to logistics," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 1(2), pages 141-153, June.
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    Cited by:

    1. Alves, P.R.L., 2022. "Quantifying chaos in stock markets before and during COVID-19 pandemic from the phase space reconstruction," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 202(C), pages 480-499.

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