Sliding Mode Control for Variable-Speed Trajectory Tracking of Underactuated Vessels with TD3 Algorithm Optimization
সংরক্ষণ করুন:
| প্রকাশিত: | Journal of Marine Science and Engineering vol. 13, no. 1 (2025), p. 99 |
|---|---|
| প্রধান লেখক: | |
| অন্যান্য লেখক: | , , |
| প্রকাশিত: |
MDPI AG
|
| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| ট্যাগগুলো: |
কোনো ট্যাগ নেই, প্রথমজন হিসাবে ট্যাগ করুন!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3159529905 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2077-1312 | ||
| 024 | 7 | |a 10.3390/jmse13010099 |2 doi | |
| 035 | |a 3159529905 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231479 |2 nlm | ||
| 100 | 1 | |a Zhu, Shiya | |
| 245 | 1 | |a Sliding Mode Control for Variable-Speed Trajectory Tracking of Underactuated Vessels with TD3 Algorithm Optimization | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a An adaptive sliding mode controller (SMC) design with a reinforcement-learning parameter optimization method is proposed for variable-speed trajectory tracking control of underactuated vessels under scenarios involving model uncertainties and external environmental disturbances. First, considering the flexible control requirements of the vessel’s propulsion system, the desired navigation speed is designed to satisfy an S-curve acceleration and deceleration process. The rate of change of the trajectory parameters is derived. Second, to address the model uncertainties and external disturbances, an extended state observer (ESO) is designed to estimate the unknown bounded disturbances and to provide feedforward compensation. Moreover, an adaptive law is designed to estimate the upper bound of the unknown disturbances, ensuring system stability even in the presence of asymptotic observation errors. Finally, the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm is employed for real-time controller parameter tuning. Numerical simulation results demonstrate that the proposed method significantly improves the trajectory tracking accuracy and dynamic response speed of the underactuated vessel. Specifically, for a sinusoidal trajectory with an amplitude of 200 m and a frequency of 0.01, numerical results show that the proposed method achieves convergence of the longitudinal tracking error to zero, while the lateral tracking error remains stable within 1 m. For the circular trajectory with a radius of 300 m, the numerical results indicate that both the longitudinal and lateral tracking errors are stabilized within 1 m. Compared with the fixed-value sliding mode controller, the proposed method demonstrates superior trajectory tracking accuracy and smoother control performance. | |
| 653 | |a Mathematical analysis | ||
| 653 | |a Dynamic response | ||
| 653 | |a Algorithms | ||
| 653 | |a S curves | ||
| 653 | |a Ecosystem disturbance | ||
| 653 | |a Navigation | ||
| 653 | |a Tracking | ||
| 653 | |a Motion control | ||
| 653 | |a Propulsion systems | ||
| 653 | |a Systems stability | ||
| 653 | |a Machine learning | ||
| 653 | |a Vessels | ||
| 653 | |a Parameter uncertainty | ||
| 653 | |a Tracking errors | ||
| 653 | |a Energy consumption | ||
| 653 | |a State observers | ||
| 653 | |a Adaptive algorithms | ||
| 653 | |a Accuracy | ||
| 653 | |a Adaptive systems | ||
| 653 | |a Embedded systems | ||
| 653 | |a Asymptotic methods | ||
| 653 | |a Control algorithms | ||
| 653 | |a Disturbances | ||
| 653 | |a Trajectory optimization | ||
| 653 | |a Control systems design | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Upper bounds | ||
| 653 | |a Tracking control | ||
| 653 | |a Neural networks | ||
| 653 | |a Controllers | ||
| 653 | |a Observers | ||
| 653 | |a Design | ||
| 653 | |a Sliding mode control | ||
| 653 | |a Slumping | ||
| 653 | |a Acceleration | ||
| 653 | |a Real time | ||
| 653 | |a Design optimization | ||
| 653 | |a Mathematical models | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Zhang, Gang | |
| 700 | 1 | |a Wang, Qin | |
| 700 | 1 | |a Li, Zhengyu | |
| 773 | 0 | |t Journal of Marine Science and Engineering |g vol. 13, no. 1 (2025), p. 99 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3159529905/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3159529905/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3159529905/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |