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A novel combination of fuzzy PID and deep neural controller in feedback-error-learning framework

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  • Baghelani, Esfandiar
  • Teshnehlab, Mohammad
  • Roshanian, Jafar

Abstract

This paper proposes a novel hybrid control strategy that integrates fuzzy logic, deep neural networks (DNNs), and classical proportional-integral-derivative (PID) control within the feedback-error-learning (FEL) framework. The proposed method dynamically adapts PID parameters using a fuzzy inference system (FIS) while employing a DNN to learn the system's inverse dynamics, thereby enhancing adaptability and robustness. Notable features include offline pre-training of the DNN using data from PID and FIS-tuned PID controllers, a novel single-sample normalization layer for DNN input preprocessing, and the seamless integration of FIS-adapted PID gains within the FEL framework. Extensive simulations demonstrate significant average improvements over other control methods. Specifically, the proposed method achieves average reductions of 53 % in steady-state error (SSE), 21 % in rise time, 41 % in mean absolute error (MAE), and 21 % lowering of control effort, indicating enhanced disturbance rejection and efficient control effort under uncertainties. These results validate the proposed hybrid framework as a versatile and efficient solution for diverse industrial and engineering applications.

Suggested Citation

  • Baghelani, Esfandiar & Teshnehlab, Mohammad & Roshanian, Jafar, 2025. "A novel combination of fuzzy PID and deep neural controller in feedback-error-learning framework," Chaos, Solitons & Fractals, Elsevier, vol. 194(C).
    • Handle: RePEc:eee:chsofr:v:194:y:2025:i:c:s0960077925002632
    DOI: 10.1016/j.chaos.2025.116250
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    References listed on IDEAS

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    1. Hadipour Lakmesari, S. & Mahmoodabadi, M.J. & Yousef Ibrahim, M., 2021. "Fuzzy logic and gradient descent-based optimal adaptive robust controller with inverted pendulum verification," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    2. Tian, Binbin & Peng, Hui & Kang, Tiao, 2024. "RBF-ARX model-based predictive control approach to an inverted pendulum with self-triggered mechanism," Chaos, Solitons & Fractals, Elsevier, vol. 186(C).
    3. Zeghlache, Samir & Ghellab, Mohammed Zinelaabidine & Djerioui, Ali & Bouderah, Brahim & Benkhoris, Mohamed Fouad, 2023. "Adaptive fuzzy fast terminal sliding mode control for inverted pendulum-cart system with actuator faults," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 210(C), pages 207-234.
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