On Learning a Control System without Continuous Feedback

We discuss a class of control problems by means of deep neural networks (DNN). Our goal is to develop DNN models that, once trained, are able to produce solutions of such problems at an acceptable error-rate and much faster computation time than an ordinary numerical solver. In the present note we study two such models for the Brockett integrator control problem.

  • Published in:
    ESANN European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
  • Type:
    Inproceedings
  • Authors:
    B. Georgiev, G. Angelov
  • Year:
    2020

Citation information

B. Georgiev, G. Angelov: On Learning a Control System without Continuous Feedback, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), ESANN, 2020, Georgiev.Angelov.2020,