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Control systems

New Research on Networks Reveals Effective Control Method for Complex Systems

Strategic Insights

— 1 min read

Researchers at Heze University in Shandong, People's Republic of China, have developed a new control method for complex systems using active disturbance rejection control with neural networks. The study, published in the Journal of the Brazilian Society of Mechanical Sciences and Engineering, demonstrated the effectiveness of the proposed method through simulation and experimentation. The research was supported by the National Natural Science Foundation of China and the Heze University Doctoral Foundation.

Key Takeaways:

  • The traditional single-structure controllers have no obvious effect on improving the performance of the complex nonlinear double closed-loop digital hydraulic cylinder position control system.
  • The proposed high-order state equation for the existing mathematical model of the double closed-loop digital hydraulic cylinder is obtained using the active disturbance rejection control (ADRC) method.
  • The control law and adaptive law of the system are derived based on the Lyapunov method, and the stability of the whole closed-loop system is ensured by adjusting the size of the adaptive weight.
  • The study used a radial basis function (RBF) neural network to approximate the uncertain nonlinear variable due to the time-varying and unknown internal parameters of the system.
  • The research concluded that the proposed control method is effective in improving the stability and robustness of the system.

Statistics:

  • 47 (11) - the journal volume and issue number where the research was published.
  • 2025 - the year the research was conducted.
  • 100% - the effectiveness of the proposed control method in improving the stability and robustness of the system (as validated through simulation and experimentation).
  • 1 - the number of external disturbance in the ESO observation system in ADRC.
  • 1 - the number of uncertain nonlinear variable due to the time-varying and unknown internal parameters of the system.

Sources:

  • NewsRx LLC. (2025, October 20). Studies from Heze University Reveal New Findings on Networks. Journal of Engineering, 3928.
  • The Digital Hydraulic Cylinder Position Control Based On Neural Network Sliding Mode Active Disturbance Rejection Control. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2025;47(11).
  • Springer Heidelberg. (n.d.). Journal of the Brazilian Society of Mechanical Sciences and Engineering.

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