Adaptive Nonlinear PID Control of DC Motor Position Using a Polynomial Fuzzy Long Short-Term Memory Neural Network

Ali Rospawan; Clara Lavita Angelina; I Made Andik Setiawan; Zanu Saputra; Ocsirendi; Aan Febriansyah; Indra Dwisaputra; Robert Napitupulu

doi:10.15282/ijame.22.3.2025.20.0977

Authors

Ali Rospawan Department of Electronics Engineering, Politeknik Manufaktur Negeri Bangka Belitung, Sungailiat, 33215, Indonesia
Clara Lavita Angelina Department of Electronics Engineering, Politeknik Manufaktur Negeri Bangka Belitung, Sungailiat, 33215, Indonesia
I Made Andik Setiawan Department of Electronics Engineering, Politeknik Manufaktur Negeri Bangka Belitung, Sungailiat, 33215, Indonesia
Zanu Saputra Department of Electronics Engineering, Politeknik Manufaktur Negeri Bangka Belitung, Sungailiat, 33215, Indonesia
Ocsirendi Department of Electronics Engineering, Politeknik Manufaktur Negeri Bangka Belitung, Sungailiat, 33215, Indonesia
Aan Febriansyah Department of Electronics Engineering, Politeknik Manufaktur Negeri Bangka Belitung, Sungailiat, 33215, Indonesia
Indra Dwisaputra Department of Electronics Engineering, Politeknik Manufaktur Negeri Bangka Belitung, Sungailiat, 33215, Indonesia
Robert Napitupulu Department of Mechanical Engineering, Politeknik Manufaktur Negeri Bangka Belitung, Sungailiat, 33215, Indonesia

DOI:

https://doi.org/10.15282/ijame.22.3.2025.20.0977

Keywords:

Adaptive PID control, LSTM neural networks, Polynomial fuzzy system, Position control, System identification

Abstract

This paper presents an adaptive nonlinear PID control approach enhanced by a polynomial fuzzy long short-term memory (LSTM) neural network-based system identification, addressing the limitations of traditional PID controllers in nonlinear systems. The proposed PFLSTM-ANPID integrates polynomial fuzzy modeling with LSTM-based learning, dynamically adjusting PID gains in real-time without requiring prior knowledge of system dynamics. This innovative adaptive mechanism enhances control precision, disturbance rejection, and real-time adaptability in complex, time-varying environments. Simulation studies on a highly nonlinear system demonstrate that the proposed controller significantly outperforms traditional PID, achieving 80.51% reduction in Root Mean Square Error (RMSE), 81.28% reduction in Integral Absolute Error (IAE), and 85.39% reduction in Integral Time Absolute Error (ITAE). Additionally, it surpasses advanced adaptive controllers, including SISO-CFMFAC variants, attaining the lowest IAE of 98.502. Experimental validation on a DC motor position control task further confirms the controller’s superiority, showing a 47.85% improvement in RMSE, 68.91% in IAE and a 78.57% improvement in ITAE compared to traditional PID, and outperforming a fuzzy neural network-based adaptive PID (FNN-APID) controller across all metrics. The proposed approach offers a robust, scalable, and practical solution for industrial applications where system parameters fluctuate significantly over time, representing a meaningful advancement in adaptive control techniques.

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