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

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...

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Bibliographic Details
Published in:Neural processing letters 2023-06, Vol.55 (3), p.3045-3079
Main Authors: Sanatel, Çağatay, Günel, Gülay Öke
Format: Article
Language:English
Subjects:
Adaptation
Artificial Intelligence
Complex Systems
Computational Intelligence
Computer Science
Control algorithms
Control systems design
Controllers
Cost function
Datasets
Deep learning
Design
Identification
Machine learning
Mathematical models
Nonlinear systems
Proportional integral derivative
Robust control
Self tuning
Signal processing
System identification
Tracking errors
Tracking problem
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Summary: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.
ISSN:1370-4621
1573-773X
DOI:10.1007/s11063-022-10997-1