• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid
The Transactions of the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.
SCImago Journal & Country Rank

Journal Search

  1. Home
  2. Archive
  3. 2023-12 (Vol.72 No.12)
  4. 10.5370/KIEE.2023.72.12.1705
XML PDF INFO REF

References

1 
N. Muskinja, B. Tovornik, April 2006, Swinging Up and Stabilization of a Real Inverted Pendulum, in IEEE Transactions on Industrial Electronics, Vol. 53, No. 2, pp. 631-639DOI
2 
Y. Otani, T. Kurokami, A. Inoue, Y. Hirashima, 2001, A Swingup Control of an Inverted Pendulum with Cart Position Control, IFAC Proceedings, Vol. 34, pp. 395-400DOI
3 
K. Graicehn, M. Treuer, M. Zeitz, 2007, Swing-up of the Double Pendulum on a Cart by Feedforward and Feedback Control with Experimental Validation, Automatica, Vol. 43, pp. 63-71DOI
4 
J. Kober, J. A. Bagnell, J. Peters, 2013, Reinforcement Learning in Robotics: A Survey, The International Journal of Robotics Research, Vol. 32, pp. 1238-1274DOI
5 
S. Israilov, L. Fu, J. Sánchez-Rodríguez, F. Fusco, G. Allibert, C. Raufaste, A. Médéric, 2023, Reinforcement Learning Approach to Control an Inverted Pendulum: A General Framework for Educational Purposes, PLoS ONE, Vol. 18, No. e0280071DOI
6 
J. Baek, C. Lee, Y. S. Lee, S. Jeon, S. Han, 2024, Reinforcement Learning to Achieve Real-time Control of Triple Inverted Pendulum, Engineering Applications of Artificial Intelligence, Vol. 128, No. 107518DOI
7 
Y. Gil, J. H. Park, J. Baek, S. Han, 2022, Quantization- aware Pruning Criterion for Industrial Applications, IEEE Transactions on Industrial Electronics, Vol. 69, No. 3, pp. 3203-3213DOI
8 
J. Baek, H. Jun, J. Park, H. Lee, S. Han, 2021, Sparse Variational Deterministic Policy Gradient for Continuous Real-time Control, IEEE Transactions on Industrial Electronics, Vol. 68, No. 10, pp. 9800-9810DOI
9 
G. Dulac-Arnold, D. Mankowitz, T. Hester, 2019, Challenges of Real-world Reinforcement Learning, arXiv preprint arXiv:1904.12901Google Search
10 
W. Zhao, J. P. Queralta, T. Westerlund, 2020, Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey, 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 737-744DOI
11 
N. Jakobi, P. Husbands, 1995, Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics, Advances in Artificial Life: Third European Conference on Artificial Life Granada, pp. 704-720Google Search
12 
T. Glück, A. Eder, A. Kugi, 2013, Swing-up Control of a Triple Pendulum on a Cart with Experimental Validation, Automatica, Vol. 49, pp. 801-808DOI
13 
D. Ju, C. Choi, J. Jeong, Y. S. Lee, 2022, Design and Parameter Estimation of a Double Inverted Pendulum for Model-based Swing-up Control, Journal of Institute of Control, Robotics and Systems (in Korean), Vol. 28, No. 9, pp. 793-803Google Search
14 
T. Lee, D. Ju, Y. S. Lee, 2023, Development Environment of Reinforcement Learning-based Controllers for Real-world Physical Systems Using LW-RCP, Journal of Institute of Control, Robotics and Systems (in Korean), Vol. 29, No. 7, pp. 543-549Google Search
15 
T. Haarnoja, A. Zhou, P. Abbeel, 2018, Soft Actor-critic: Off-policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor, International conference on machine learning. PMLR, pp. 1861-1870Google Search
16 
D. P. Kingma., 2014, Adam: A Method for Stochastic Optimization, arXiv preprint arXiv:1412.6980Google Search
17 
J. Jeong, D. Ju, Y. Fujiyama, Y. S. Lee, 2023, Transition Control of a Double Inverted Pendulum Using an LW-RCP, Journal of Institute of Control, Robotics and Systems (in Korean), Vol. 29, No. 9, pp. 694-703Google Search