Digital twin syncing for autonomous surface vessels using reinforcement learning and nonlinear model predictive control
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces adaptive control for autonomous surface vessels (ASVs) using deep reinforcement learning (DRL) and nonlinear model predictive control (NMPC). This enhances ASV reliability and digital twin synchronization in dynamic maritime environments.
Area Of Science
- Maritime robotics
- Control engineering
- Artificial intelligence
Background
- Current autonomous surface vessel (ASV) control systems struggle with model uncertainties and parameter variations.
- This limitation compromises ASV reliability in complex, dynamic maritime conditions, necessitating adaptive solutions.
Purpose Of The Study
- To develop an integrated deep reinforcement learning (DRL) and nonlinear model predictive control (NMPC) approach for ASV control.
- To ensure continuous synchronization between the ASV's digital twin and its physical counterpart for enhanced accuracy and adaptability.
- To optimize ASV control performance and identify unknown model parameters in real-time.
Main Methods
- Integration of DRL for optimizing NMPC parameters and identifying unknown model parameters.
- Utilizing digital twins for risk-free training of control agents in simulated maritime environments.
- Real-time parameter identification and NMPC tuning via DRL.
Main Results
- Demonstrated effectiveness of the DRL-NMPC approach in improving ASV control performance.
- Enhanced reliability and adaptability of ASVs under dynamic and uncertain maritime conditions.
- Successful synchronization of digital twins with physical ASVs, validated through extensive simulations.
Conclusions
- The proposed DRL-NMPC framework significantly enhances ASV safety, efficiency, and reliability.
- This approach provides a robust solution for ASV control challenges in dynamic environments.
- The study lays the groundwork for advanced autonomous maritime navigation and control systems.
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