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Multivariate Gaussian process-based learning model predictive control with unscented Kalman filter for autonomous

Zhi-Jie Wu1, Li-Ying Hao1

  • 1College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, People's Republic of China.

ISA Transactions
|January 11, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces multivariate Gaussian process regression (MVGPR) for modeling autonomous surface vehicle (ASV) dynamics. The developed learning-based model predictive control (MPC) ensures robust trajectory tracking, even with communication denial-of-service (DoS) attacks.

Keywords:
Autonomous surface vehiclesDenial-of-service attacksModel predictive controlMultivariate Gaussian process regressionUnscented Kalman filter

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Area of Science:

  • Robotics and Control Systems
  • Machine Learning for Autonomous Systems
  • Marine Engineering

Background:

  • Modeling nonlinear dynamics of autonomous surface vehicles (ASVs) is challenging due to hydrodynamic effects and environmental uncertainties.
  • Traditional methods struggle with high-dimensional data and uncertainty estimation in ASV systems.
  • Denial-of-service (DoS) attacks pose a significant threat to the reliability of ASV communication networks.

Purpose of the Study:

  • To develop a robust trajectory tracking control scheme for ASVs using advanced machine learning techniques.
  • To accurately model ASV dynamics and estimate system uncertainties in complex maritime environments.
  • To enhance ASV resilience against communication disruptions like DoS attacks.

Main Methods:

  • Multivariate Gaussian Process Regression (MVGPR) was employed to model the ASV's system state and observation dynamics, enabling accurate multi-input, multi-output correlation and uncertainty estimation.
  • An Unscented Kalman Filter (UKF) was designed for improved state estimation, ensuring robustness even for unmeasurable states.
  • A learning-based Model Predictive Control (MPC) framework utilizing MVGPR was developed to handle trajectory tracking and mitigate the impact of DoS attacks without external compensators.

Main Results:

  • The MVGPR approach effectively modeled complex ASV dynamics, outperforming traditional methods in high-dimensional settings.
  • The integrated UKF enhanced state estimation accuracy and robustness.
  • The MVGPR-based learning MPC demonstrated robust and precise trajectory tracking performance, improving system stability under uncertain conditions and simulated DoS attacks.

Conclusions:

  • The proposed MVGPR-based learning MPC framework offers a significant advancement in autonomous surface vehicle control, providing accurate modeling and robust trajectory tracking.
  • The method effectively addresses challenges posed by complex dynamics, environmental uncertainties, and communication security threats.
  • Validation through simulations and hardware experiments confirms the practical applicability and effectiveness of the developed control strategy.