Work

Event-Based Robust Reinforcement Learning Course Tracking for MSVs with Actuator Faults

Year: 2026

Type: article

Abstract: This paper investigates the robust course control for marine surface vehicles (MSVs) by using reinforcement learning and a dynamic event-triggered mechanism under rudder faults. A fault-tolerant optimized tracking control scheme is designed based on reinforcement learning. Firstly, a general actuator fault model is established. Secondly, internal and external disturbances as well as fault dynamics are aggregated into lumped uncertainties, and a fault observer is constructed to estimate and compensate the lumped uncertainty term. Then, a tracking cost function for the MSV system is constructed, and a reinforcement learning framework with a single critic network is used to approximate the value function and generate a learning-based compensation input. Finally, a dynamic event-triggered mechanism is implemented between the control layer and the execution layer to reduce the triggering frequency of actuators. Lyapunov stability theory is utilized to prove that all signals within the closed-loop system are uniformly ultimately bounded. Simulation results verify that the proposed control scheme achieves favorable overall performance.

Source: Journal of Marine Science and Engineering

DOI: 10.3390/jmse14212093

Authors: Xiaoyang Gao, Lirong Kou, Dakuan Song, Quan Shi, Xin Hu

Institutions: Dalian Maritime University, Ludong University

Language: English

Topic: Adaptive Dynamic Programming Control

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: National Natural Science Foundation of China, Natural Science Foundation of Liaoning Province, Liaoning Revitalization Talents Program, Fundamental Research Funds for the Central Universities