Name
Title | ||
|---|---|---|
Reinforcement learning control for underactuated surface vessel with output error constraints and uncertainties |
Abstract | ||
|---|---|---|
•We present a reinforcement-learning-based trajectory tracking method for an underactuated surface vessel with model uncertainties and disturbances.•A novel reinforcement learning method based on Actor-Critic neural network structure is proposed to guarantee the convergence of tracking errors.•Error transformation function is introduced to tackle the output constraint problem which ensures the tracking errors stay in the constraint boundaries.•A novel critic function comprised of the primary critic signal and a second critic signal instead of the long-term cost function to supervise the tracking performance and tune the weights of the actor NN.•The Actor-Critic neural network structure, which does not rely on the system model and does not need off-line training, is used to approximate and compensate the system unknown nonlinear dynamics and disturbances, so as to improve the system performance. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neucom.2020.03.021 | Neurocomputing |
Keywords | DocType | Volume |
Reinforcement learning,Actor-Critic (AC),Output constraints,Underactuated marine vessel,Trajectory tracking,Neural networks | Journal | 399 |
ISSN | Citations | PageRank |
0925-2312 | 3 | 0.39 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zewei Zheng | 1 | 4 | 2.09 |
| Linping Ruan | 2 | 3 | 0.39 |
| Ming Zhu | 3 | 3 | 1.74 |
| Xiao Guo | 4 | 3 | 1.06 |