IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i20p3185-d1496739.html
   My bibliography  Save this article

Implementing PSO-LSTM-GRU Hybrid Neural Networks for Enhanced Control and Energy Efficiency of Excavator Cylinder Displacement

Author

Listed:
  • Van-Hien Nguyen

    (Graduate School of Mechanical and Automotive Engineering, University of Ulsan, Ulsan 44610, Republic of Korea)

  • Tri Cuong Do

    (College of Technology and Design, University of Economics Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam)

  • Kyoung-Kwan Ahn

    (School of Mechanical Engineering, University of Ulsan, Ulsan 44610, Republic of Korea)

Abstract

In recent years, increasing attention has been given to reducing energy consumption in hydraulic excavators, resulting in extensive research in this field. One promising solution has been the integration of hydrostatic transmission (HST) and hydraulic pump/motor (HPM) configurations in parallel systems. However, these systems face challenges such as noise, throttling losses, and leakage, which can negatively impact both tracking accuracy and energy efficiency. To address these issues, this paper introduces an intelligent real-time prediction framework for system positioning, incorporating particle swarm optimization (PSO), long short-term memory (LSTM), a gated recurrent unit (GRU), and proportional–integral–derivative (PID) control. The process begins by analyzing real-time system data using Pearson correlation to identify hyperparameters with medium to strong correlations to the positioning parameters. These selected hyperparameters are then used as inputs for forecasting models. Independent LSTM and GRU models are subsequently developed to predict the system’s position, with PSO optimizing four key hyperparameters of these models. In the final stage, the PSO-optimized LSTM-GRU models are employed to perform real-time intelligent predictions of motion trajectories within the system. Simulation and experimental results show that the model achieves a prediction deviation of less than 3 mm, ensuring precise real-time predictions and providing reliable data for system operators. Compared to traditional PID and LSTM-GRU-PID controllers, the proposed controller demonstrated superior tracking accuracy while also reducing energy consumption, achieving energy savings of up to 10.89% and 2.82% in experimental tests, respectively.

Suggested Citation

  • Van-Hien Nguyen & Tri Cuong Do & Kyoung-Kwan Ahn, 2024. "Implementing PSO-LSTM-GRU Hybrid Neural Networks for Enhanced Control and Energy Efficiency of Excavator Cylinder Displacement," Mathematics, MDPI, vol. 12(20), pages 1-21, October.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:20:p:3185-:d:1496739
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/20/3185/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/20/3185/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zohaib Ahmad & Jianqiang Li & Tariq Mahmood, 2023. "Adaptive Hyperparameter Fine-Tuning for Boosting the Robustness and Quality of the Particle Swarm Optimization Algorithm for Non-Linear RBF Neural Network Modelling and Its Applications," Mathematics, MDPI, vol. 11(1), pages 1-16, January.
    2. Do, Tri Cuong & Dinh, Truong Quang & Yu, Yingxiao & Ahn, Kyoung Kwan, 2023. "Innovative powertrain and advanced energy management strategy for hybrid hydraulic excavators," Energy, Elsevier, vol. 282(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Thanh Ha Nguyen & Tri Cuong Do & Van Du Phan & Kyoung Kwan Ahn, 2023. "Working Performance Improvement of a Novel Independent Metering Valve System by Using a Neural Network-Fractional Order-Proportional-Integral-Derivative Controller," Mathematics, MDPI, vol. 11(23), pages 1-21, November.
    2. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2023. "Particle swarm optimization of Elman neural network applied to battery state of charge and state of health estimation," Energy, Elsevier, vol. 285(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:20:p:3185-:d:1496739. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.