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

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Abstract

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 \(K_P\), \(K_I\) and \(K_D\) 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.

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Authors and Affiliations

  1. Department of Software Design, Turkish Aerospace Industries, Istanbul, Turkey

    Çağatay Sanatel

  2. Control and Automation Engineering Department, Istanbul Technical University, Istanbul, Turkey

    Gülay Öke Günel

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  1. Çağatay Sanatel
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  2. Gülay Öke Günel
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Correspondence to Çağatay Sanatel.

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Sanatel, Ç., Günel, G.Ö. Long Short Term Memory Based Self Tuning Regulator Design for Nonlinear Systems. Neural Process Lett 55, 3045–3079 (2023). https://doi.org/10.1007/s11063-022-10997-1

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