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Data-driven adaptive integral variable structure control method for AGV trajectory tracking system based on BP neural

Jianliang Xu1, Jin Qian2

  • 1School of Mechanical and Electrical Engineering, Quzhou College of Technology, Quzhou, 324000, China.

Scientific Reports
|June 15, 2026
PubMed
Summary
This summary is machine-generated.

A new data-driven adaptive control method enhances automated guided vehicle (AGV) accuracy and robustness. This approach improves tracking and disturbance rejection, outperforming traditional control strategies.

Keywords:
Automated guided vehicleDynamic linearizationNeural network observerSliding mode controlTrajectory tracking control

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Automated Guided Vehicles (AGVs) face challenges in control accuracy and robustness due to nonlinearities and external disturbances.
  • Existing control methods often rely on precise system models, which are difficult to obtain for complex, time-varying AGV dynamics.

Purpose of the Study:

  • To propose a novel data-driven adaptive discrete-time integral sliding mode control (ADISMC) method for AGVs.
  • To enhance control accuracy and robustness under nonlinear, time-varying conditions and external disturbances.
  • To mitigate chattering and improve overall AGV performance.

Main Methods:

  • Full-Form Dynamic Linearization (FFDL) to create a compact dynamic model from I/O data, avoiding mechanistic modeling.
  • A BP Neural Network Observer (BPNNO) for online disturbance estimation and feedforward compensation, reducing switching gain and chattering.
  • An adaptive reaching law using tracking error for balanced response speed and control smoothness.

Main Results:

  • Theoretical analysis confirmed boundedness of the pseudo gradient and finite-time convergence of sliding mode motion.
  • AGV kinematic simulations showed the ADISMC method significantly outperformed PID and MFAC.
  • Under disturbance-free conditions, maximum heading tracking error was 0.3488 rad; with white noise disturbance, mean square error was 14% of the PID scheme's.

Conclusions:

  • The proposed ADISMC method offers superior tracking accuracy and disturbance rejection robustness for AGVs.
  • The data-driven approach effectively handles nonlinearities and external disturbances without requiring detailed system models.
  • This method presents a promising solution for improving the reliability and performance of AGVs in dynamic environments.