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Feedback control systems01:26

Feedback control systems

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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Related Experiment Video

Updated: Apr 6, 2026

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
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Data-driven nonlinear model predictive control for AUV trajectory tracking under oceanic disturbances.

Kang Zou1, Xiujing Gao2, Yanjie Chen3

  • 1School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China; School of Smart Marine and Engineering, Fujian University of Technology, Fuzhou, 350108, China.

ISA Transactions
|April 5, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a unified data-driven nonlinear model predictive control (DD-NMPC) framework for autonomous underwater vehicles (AUVs). It enhances trajectory tracking by integrating online model updates, disturbance estimation, and stability constraints for improved performance.

Keywords:
Autonomous underwater vehiclesData-driven NMPCRecursive least squaresSliding mode observerTrajectory tracking

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Ocean Engineering

Background:

  • Autonomous Underwater Vehicles (AUVs) face challenges in trajectory tracking due to modeling uncertainties, unpredictable ocean disturbances, and actuator limitations.
  • Existing control methods often struggle to adapt to time-varying conditions and guarantee stability under constraints.

Purpose of the Study:

  • To develop a unified data-driven nonlinear model predictive control (DD-NMPC) framework for robust trajectory tracking of AUVs.
  • To address challenges posed by modeling uncertainties, unknown ocean-wave disturbances, and actuator saturation.
  • To enhance control performance, stability, and robustness in complex underwater environments.

Main Methods:

  • A unified DD-NMPC framework coordinating online model updating, disturbance estimation, and Lyapunov-constrained predictive control.
  • An RLS/VRF-based scheme for online parameter estimation and model reconstruction to adapt to time-varying hydrodynamic effects.
  • A dual-layer nested adaptive sliding-mode disturbance observer (DLN-ASMDO) for estimating ocean-wave disturbances without prior bound information.
  • Embedding Lyapunov-decrease conditions and input constraints within the NMPC formulation for stability and recursive feasibility.

Main Results:

  • The developed DD-NMPC framework demonstrated improved trajectory-tracking accuracy for AUVs.
  • Effective attenuation of time-varying ocean-wave disturbances was achieved through integrated disturbance estimation and feedforward compensation.
  • Enhanced control smoothness and robustness were observed under actuator saturation and modeling uncertainties.
  • Lyapunov analysis confirmed the stability and recursive feasibility of the proposed control strategy.

Conclusions:

  • The unified DD-NMPC framework offers a robust and adaptive solution for AUV trajectory tracking in challenging conditions.
  • The integration of online learning, disturbance rejection, and stability guarantees significantly advances AUV control capabilities.
  • This approach reduces reliance on precise pre-identified models and provides reliable performance despite environmental and system uncertainties.