Long Short Term Memory Based Self Tuning Regulator Design for Nonlinear Systems

Çağatay Sanatel*, Gülay Öke Günel

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In this paper, a Long Short Term Memory (LSTM) based Self Tuning Regulator (STR) for trajectory tracking problem of nonlinear systems is proposed. In the STR, a Proportional Integral Derivative (PID) controller is used as an adaptive parametric controller. The system model is estimated at every time step since it is utilized in computing the system Jacobian, hence controller design involves an inherent system identification problem. In the proposed architecture, LSTM is employed for both system model estimation and for updating the parameters of the PID controller. Namely, the KP, KI and KD gains are computed at every time step by LSTM, so that a cost function which is obtained from tracking error is minimized. The performance of the proposed method has been evaluated on two different nonlinear systems by extensive simulations. Simulation results justify the success of the introduced control architecture.

Original languageEnglish
Pages (from-to)3045-3079
Number of pages35
JournalNeural Processing Letters
Issue number3
Publication statusPublished - Jun 2023

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.


  • Adaptive PID Controller
  • Long short term memory
  • Self tuning regulator
  • System identification


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