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Volume 11 Issue 5
May  2024

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  >  11(5): 1227-1238
Q. Yang, F. Zhang, and  C. Wang,  “Deterministic learning-based neural PID control for nonlinear robotic systems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 5, pp. 1227–1238, May 2024. doi: 10.1109/JAS.2024.124224
Citation: Q. Yang, F. Zhang, and  C. Wang,  “Deterministic learning-based neural PID control for nonlinear robotic systems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 5, pp. 1227–1238, May 2024. doi: 10.1109/JAS.2024.124224
shu

Deterministic Learning-Based Neural PID Control for Nonlinear Robotic Systems

doi: 10.1109/JAS.2024.124224
Funds:  This work was supported by the National Natural Science Foundation of China (62203262, 62350083); Natural Science Foundation of Shandong Province (ZR2020ZD40, ZR2022QF124)
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    Abstract

  • Traditional proportional-integral-derivative (PID) controllers have achieved widespread success in industrial applications. However, the nonlinearity and uncertainty of practical systems cannot be ignored, even though most of the existing research on PID controllers is focused on linear systems. Therefore, developing a PID controller with learning ability is of great significance for complex nonlinear systems. This article proposes a deterministic learning-based advanced PID controller for robot manipulator systems with uncertainties. The introduction of neural networks (NNs) overcomes the upper limit of the traditional PID feedback mechanism’s capability. The proposed control scheme not only guarantees system stability and tracking error convergence but also provides a simple way to choose the three parameters of PID by setting the proportional coefficients. Under the partial persistent excitation (PE) condition, the closed-loop system unknown dynamics of robot manipulator systems are accurately approximated by NNs. Based on the acquired knowledge from the stable control process, a learning PID controller is developed to further improve overall control performance, while overcoming the problem of repeated online weight updates. Simulation studies and physical experiments demonstrate the validity and practicality of the proposed strategy discussed in this article.

     

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    Highlights

    • A novel learning-based intelligent PID control scheme is proposed
    • The uncertainties are accurately learned in neural PID closed-loop control
    • The learned knowledge can be reused to further improve control performance
    • The complexity of traditional PID parameters selection is weakened to some extent
    • Simulation and physical experiments verified the validity of the proposed method

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