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Examples of MLC Problem Solutions

In: Machine Learning Control by Symbolic Regression

Author

Listed:
  • Askhat Diveev

    (Russian Academy of Sciences (FRC CSC RAS), Federal Research Center “Computer Science and Control”)

  • Elizaveta Shmalko

    (Russian Academy of Sciences (FRC CSC RAS), Federal Research Center “Computer Science and Control”)

Abstract

This chapter contains various applied examples of solving machine learning control problems by various methods of symbolic regression presented in the book. First, the tasks of unsupervised learning are considered based on the value of the target functional. The classical Pontryagin problem is considered and a comparison of the solution obtained by machine learning with the classical result is given. The problem of stabilization system synthesis for various objects is considered. Various symbolic regression methods are demonstrated. An example of solving a supervised machine learning synthesis problem is considered, where, to obtain a training sample, the optimal control problem is solved many times under different initial conditions, and then the obtained solutions are approximated by symbolic regression. An identification example is presented. An example of solving the problem of synthesized optimal control for a mobile robot in comparison with the solution of optimal control and subsequent stabilization is given. All the examples presented are aimed to show the possibilities and prospects of symbolic regression methods in machine learning control.

Suggested Citation

  • Askhat Diveev & Elizaveta Shmalko, 2021. "Examples of MLC Problem Solutions," Springer Books, in: Machine Learning Control by Symbolic Regression, chapter 0, pages 105-155, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-83213-1_5
    DOI: 10.1007/978-3-030-83213-1_5
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