Original scientific paper
https://doi.org/10.21278/brod76206
Adaptive trajectory controller design for unmanned surface vehicles based on SAC-PID
Wei Guan
; Navigation College, Dalian Maritime University, Dalian, China
*
Zhaoyong Xi
; Navigation College, Dalian Maritime University, Dalian, China
Zhewen Cui
; Navigation College, Dalian Maritime University, Dalian, China
Xianku Zhang
; Navigation College, Dalian Maritime University, Dalian, China
* Corresponding author.
Abstract
n adaptive proportional integral derivative (PID) controller based on the soft actor-critic (SAC) algorithm for trajectory control of unmanned surface vehicles (USV) is proposed in this paper. The gains of the PID controller need to be manually adjusted based on experience in the original formulation. Furthermore, once tuned, these gains remain fixed and making further modifications becomes time-consuming and labor-intensive. To address these limitations, the SAC algorithm is introduced, enabling online tuning of PID gains through agent-environment interaction. Additionally, the strategy of combining SAC algorithm with PID controller mitigates concerns regarding interpretability and security often associated with DRL. In this study, stability analysis of the adaptive trajectory controller based on the SAC-PID algorithm is conducted. This paper horizontally compares the proposed method with traditional PID tuning methods, genetic algorithms (GA), and deep deterministic policy gradient (DDPG) algorithm to highlight the superiority of the SAC-PID approach. Finally, experiments in different scenarios are performed to compare generalization capabilities between DDPG and SAC algorithms. Results demonstrate that the proposed SAC-PID algorithm exhibits excellent stability properties, fast convergence speed, and strong generalization ability.
Keywords
Deep reinforcement learning; unmanned surface vehicle; soft actor critic; PID tuning
Hrčak ID:
329831
URI
Publication date:
1.4.2025.
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