Abstract
This paper presents a safety-aware deep reinforcement learning (DRL)-based trajectory tracking control of autonomous surface vessels (ASVs). A multilayer neural network (MNN) observer estimates the ASV's state and uncertain dynamics. By utilizing the estimate state vector from the observer, a safety-aware DRL-based optimal policy is formulated using control barrier function (CBF) and Karush-Kuhn-Tucker (KKT) conditions. An actor-critic MNN with singular value decomposition (SVD)-based update mitigates vanishing gradients. To enhance adaptability, an online safe lifelong learning (SLL) scheme counters catastrophic forgetting across varying ASV dynamics. The Shapley Additive Explanations (SHAP) method identifies key features influencing the control policy. Simulations on an underactuated ASV show that SLL-based control reduces cumulative costs by 17% and RMS error by 32% compared to a baseline without SLL.
Recommended Citation
B. Farzanegan and S. Jagannathan, "Multi-Model Safe Neuro-Optimal Output Tracking Control of Autonomous Surface Vessels with Explainable AI," 2025 IEEE Conference on Control Technology and Applications Ccta 2025, pp. 540 - 545, Institute of Electrical and Electronics Engineers, Jan 2025.
The definitive version is available at https://doi.org/10.1109/CCTA53793.2025.11151388
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Keywords and Phrases
Approximate dynamic programming; Autonomous Surface Vessels; Deep reinforcement learning; Safety
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2025
Included in
Computer Sciences Commons, Electrical and Computer Engineering Commons, Medicine and Health Sciences Commons
Comments
Office of Naval Research, Grant N00014-23-1-2195