Reinforcement learning-based adaptive PID controller for DPS
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
If a lot of previous transitions are stored in history, then actions that had high probability in the past can have low probability under the current policy yields large negative losses.
“If a lot of previous transitions are stored in history, then actions that had high probability in the past can have low probability under the current policy yields large negative losses [8].”
From Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system (2025) · cites this paper as [8]
Reducing replay history size results in correlated updates, which is also a problem.
“This is because reducing replay history size that will result in correlated updates, which is also a problem.”
From Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system (2025) · cites this paper as [8]
To learn complex systems, large states are needed that take more time to learn.
“To learn complex systems, large states are needed that take more time to learn.”
From Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system (2025) · cites this paper as [8]
These states take up a lot of memory due to backup tables and correlations between samples.
“These states take up a lot of memory due to backup tables and correlations between samples.”
From Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system (2025) · cites this paper as [8]
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273 · see citing works
Watkins, C., & Dayan, P. (1992) · Machine-mediated learning · doi:10.1023/a:1022676722315 · Cited by 14,277 · see citing works
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008 · see citing works
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782 · see citing works
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471 · see citing works
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083 · see citing works
Optimum Settings for Automatic Controllers
Ziegler, J. G., & Nichols, N. B. (1942) · Transactions of the American Society of Mechanical Engineers · doi:10.1115/1.4019264 · Cited by 5,230 · see citing works
Deterministic Policy Gradient Algorithms
Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., & Riedmiller, M. (2014) · HAL · Cited by 3,275 · see citing works
Maciejowski, J. M. (1989) · Cited by 1,523 · see citing works
The theory of dynamic programming
Bellman, R. (1954) · Bulletin of the American Mathematical Society · doi:10.1090/s0002-9904-1954-09848-8 · Cited by 1,497 · see citing works
Maciejowsk (1990) · Addison-Wesley Longman Publishing Co., Inc. eBooks · Cited by 1,447 · see citing works
SpotTune: Transfer Learning Through Adaptive Fine-Tuning
Guo, Y., Shi, H., Kumar, A., Grauman, K., Rosing, T., & Feris, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00494 · Cited by 506 · see citing works
Davison, E. J. (1976) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1976.1101126 · Cited by 375 · see citing works
Kalman filtering for positioning and heading control of ships and offshore rigs
Fossen, T. I., & Pérez, T. (2009) · IEEE Control Systems · doi:10.1109/mcs.2009.934408 · Cited by 281 · see citing works
An adaptive deep reinforcement learning approach for MIMO PID control of mobile robots
Carlucho, I., Paula, M. D., & Acosta, G. G. (2020) · ISA Transactions · doi:10.1016/j.isatra.2020.02.017 · Cited by 163 · see citing works
Design of hybrid controller for dynamic positioning from calm to extreme sea conditions
Nguyen, D. T., Sorensen, A. J., & Quek, S. T. (2007) · Automatica · doi:10.1016/j.automatica.2006.11.017 · Cited by 140 · see citing works
Multivariable tuning regulators for unknown systems
Penttinen, J., & Koivo, H. N. (1980) · Automatica · doi:10.1016/0005-1098(80)90023-0 · Cited by 135 · see citing works
Double Q-PID algorithm for mobile robot control
Carlucho, I., Paula, M. D., & Acosta, G. G. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.06.066 · Cited by 80 · see citing works
Optimal Thrust Allocation Methods for Dynamic Positioning of Ships
de Wit, C. (2009) · Research Repository (Delft University of Technology) · Cited by 44 · see citing works
Xu, S., Wang, X., Yang, J., et al. (2019) · Journal of Marine Science and Technology · doi:10.1007/s00773-019-00689-2 · Cited by 37 · see citing works
Dynamic positioning system of semi-submersible platform using fuzzy control
Yamamoto, M. M., & MOROOKA, C. K. (2005) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1590/s1678-58782005000400014 · Cited by 29 · see citing works
Application of Recent Developments in Deep Learning to ANN-based Automatic Berthing Systems
Lee, D.-S., Lee, S., & Seo, Y.-J. (2020) · International Journal of Engineering and Technology Innovation · doi:10.46604/ijeti.2020.4354 · Cited by 25 · see citing works
Self-citationMultivariable PID tuning of dynamic ship positioning control systems
Martin, P., & Katebi, R. M. (2005) · Journal of Marine Engineering & Technology · doi:10.1080/20464177.2005.11020190 · Cited by 21 · see citing works
CrossNorm: Normalization for Off-Policy TD Reinforcement LearningPreprint
Bhatt, A., Argus, M. J., Amiranashvili, A., & Brox, T. (2019) · arXiv (Cornell University) · Cited by 17 · see citing works
Koschorrek, P., Siebert, C., Haghani, A., et al. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.10.277 · Cited by 16 · see citing works
Davison, E. J. (1975) · IEEE Conference on Decision and Control including the Symposium on Adaptive Processes (CDC) · doi:10.1109/cdc.1975.270674 · Cited by 16 · see citing works
Hassani, V., & Pascoal, A. (2015) · Operations research, computer science. Interface series · doi:10.1007/978-3-319-16133-4_17 · Cited by 11 · see citing works
Learning to Drive and Simulate Autonomous Mobile Robots
Gloye, A., Göktekin, C., Egorova, A., et al. (2005) · Lecture notes in computer science · doi:10.1007/978-3-540-32256-6_13 · Cited by 9 · see citing works
Dynamic Positioning: Early Design, Capability and Offsets—A Novel Approach
Veer, R. V. ‘., & Gachet, M. (2011) · doi:10.1115/omae2011-49354 · Cited by 8 · see citing works
The Terebel Dynamic Positioning System - Results of Five Years of Field Work and Experiments
Harbonn, J. (1971) · Offshore Technology Conference · doi:10.4043/1499-ms · Cited by 6 · see citing works
A Study on the Dynamic Positioning Control Algorithm Using Fuzzy Gain Scheduling PID Control Theory
Jeon, M.-R., Kim, H.-S., Kim, J.-H., et al. (2017) · Journal of the Society of Naval Architects of Korea · doi:10.3744/snak.2017.54.2.102 · Cited by 6 · see citing works
Autotuning Aspects for Dynamic Positioning Systems
Alme, J., & Breivik, M. (2009) · IFAC Proceedings Volumes · doi:10.3182/20090916-3-br-3001.0043 · Cited by 4 · see citing works
Correction to "The robust decentralized control of a general servomechanism problem"
Davison, E. J. (1976) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1976.1101251 · Cited by 4 · see citing works
Implementation of Adaptive Controllers Using Digital Signal Processor Chips
Teoh, E. K., & Yee, Y. E. (1991) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)51305-1 · Cited by 3 · see citing works
8th IFAC International Conference on Manoeuvring and Control of Marine Craft
(2009) · IFAC Proceedings Volumes · doi:10.3182/20090916-3-br-3001 · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system
Khater, A. A., Fekry, M., El-Bardini, M., & El-Nagar, A. M. (2025) · Neural Computing and Applications · doi:10.1007/s00521-024-10741-x · Cited by 17 · see citing works
“Also, researchers have tried to combine the advantages of RL in designing an adaptive FLC for different process control applications [7, 8].”
- Authors
- Lee, D., Lee, S. J., & Yim, S. C.
- Affiliations
- Korea Maritime and Ocean University (KR)
- Oregon State University (US)
- Corresponding author
- Lee, Seung Jae
- Venue
- Ocean Engineering
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 216
- Pages
- 108053
- ISSN
- 0029-8018 (ISSN-L)
- OpenAlex
- W3101203226
- Cited by
- 80
- References
- 37
- Citations by year
- 2026: 11 · 2025: 19 · 2024: 13 · 2023: 18 · 2022: 15 · 2021: 4
- FWCI
- 3.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Ocean Engineering (publishedVersion)
- Funders
- Korea Maritime and Ocean University
- Oregon State University
- Ministry of Trade, Industry and Energy
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Ocean Engineering › Ship Hydrodynamics and Maneuverability
- Physical Sciences › Engineering › Ocean Engineering › Maritime Navigation and Safety
- Keywords
- dynamic positioning system, adaptive PID control, deep reinforcement learning, controller gain tuning, ship control, fuzzy logic comparison
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review