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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
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    References listed on IDEAS

    as
    1. 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).
    2. 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.
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