Adaptive trajectory controller design for unmanned surface vehicles based on SAC-PID

An 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 formulati...

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Vydáno v:Brodogradnja Ročník 76; číslo 2; s. 1 - 22
Hlavní autoři: Guan, Wei, Xi, Zhaoyong, Cui, Zhewen, Zhang, Xianku
Médium: Journal Article Paper
Jazyk:angličtina
Vydáno: Sveučilište u Zagrebu Fakultet strojarstva i brodogradnje 01.01.2025
Faculty of Mechanical Engineering and Naval Architecture
Témata:
Deep reinforcement learning
PID tuning
soft actor critic
unmanned surface vehicle
ISSN:0007-215X, 1845-5859
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Abstract An 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.
AbstractList 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.
An 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.
Author Xi, Zhaoyong
Cui, Zhewen
Guan, Wei
Zhang, Xianku
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SubjectTerms Deep reinforcement learning
PID tuning
soft actor critic
unmanned surface vehicle
Title Adaptive trajectory controller design for unmanned surface vehicles based on SAC-PID
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