References
- Martinez-Piazuelo, Juan, Daniel E. Ochoa, Nicanor Quijano and Luis Felipe Giraldo. “A Multi-Critic Reinforcement Learning Method: An Application to Multi-Tank Water Systems.” IEEE Access 8 (2020): 173227–173238.
- Wameedh Riyadh Abdul-Adheem, Ibraheem Kasim Ibraheem, Decoupled control scheme for output tracking of a general industrial nonlinear MIMO system using improved active disturbance rejection scheme, Alexandria Engineering Journal, Volume 58, Issue 4,2019, Pages 1145–1156, ISSN 1110-0168
- Xu, Jin, Han Li, and Qingxin Zhang. 2023. “Multivariable Coupled System Control Method Based on Deep Reinforcement Learning” Sensors 23, no. 21: 8679.
- Ye L, Jiang P. Optimization control of the double-capacity water tank-level system using the deep deterministic policy gradient algorithm. Engineering Reports. 2023; 5(11): e12668.
- Almeida, Alexandre Marques de, Marcelo Kaminski Lenzi, and Ervin Kaminski Lenzi. 2020. “A Survey of Fractional Order Calculus Applications of Multiple-Input, Multiple-Output (MIMO) Process Control” Fractal and Fractional 4, no. 2: 22.
- Radac, Mircea-Bogdan, Radu-Emil Precup and Raul-Cristian Roman. “Data-driven model reference control of MIMO vertical tank systems with model-free VRFT and Q-Learning.” ISA transactions 73 (2018): 227–238.
- Hajare, V.D., Patre, B.M., Khandekar, A.A. et al. Decentralized PID controller design for TITO processes with experimental validation. Int. J. Dynam. Control 5, 583–595 (2017).
- Silver David, Guy Lever, Nicolas Manfred Otto Heess, Thomas Degris, Daan Wierstra and Martin A. Riedmiller. “Deterministic Policy Gradient Algorithms.” International Conference on Machine Learning (2014).
- M. D. L. Reddy, P. K. Padhy and I. Ahmad Ansari, “Auto-tuning Method for decentralized PID controller of TITO systems using firefly algorithm,” 2019 International Conference on Intelligent Computing and Control Systems (ICCS), Madurai, India, 2019, pp. 683–688.
- Khalid, Junaid & Anwari, Makbul & Khan, Muhammad & Hidayat, T. (2022). Efficient Load Frequency Control of Renewable Integrated Power System: A Twin Delayed DDPG-Based Deep Reinforcement Learning Approach. IEEE Access. 10. 1–1.
- Ould Mohamed and Mohamed Vall, Design of Decoupled PI Controllers for Two-Input Two-Output Networked Control Systems with Intrinsic and Network-Induced Time Delays. Acta Mechanica et Automatica, 15, 201–208, December 2021.
- Reshma N. Pawar and Sharad P. Jadhav Design of NDT and PSO based Decentralized PID Controller for Wood-Berry Distillation Column IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI-2017).
- Wood RK, Berry MW (1973) Terminal composition control of a binary distillation column. Chem Eng Sci 28(9):1707–1717.
- Tavakoli S, Griffin I, Fleming PJ (2006) Tuning of decentralized PI(PID) controllers for TITO processes. Control Eng Pract 14(9):1069–1080.
- Qiu C, Hu Y, Chen Y, Zeng B. Deep deterministic policy gradient (DDPG)-based energy harvesting wireless communications. IEEE Internet Things J. 2019;6(5):8577–8588.
- Yan, Z., & Xu, Y. (2020). A Multi-Agent Deep Reinforcement Learning Method for Cooperative Load Frequency Control of a Multi-Area Power System. IEEE Transactions on Power Systems, 35, 4599–4608.
- Skiparev, V., Belikov, J., Petlenkov, E., & Levron, Y. (2022). Reinforcement Learning based MIMO Controller for Virtual Inertia Control in Isolated Microgrids. 2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 1–5.
- Du, Y., Zandi, H., Kotevska, O., Kurte, K., Munk, J., Amasyali, K., Mckee, E., & Li, F. (2021). Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning. Applied Energy, 281, 116117.
- Ho, T, Tat, T, Ngo, H., Nguyen, T, Bui, D., Le, T., Le, V, & Huynh, L. (2023). Applying DDPG Algorithm to Swing-Up and Balance Control for a Double Inverted Pendulum on a Cart. Robotica & Management.
- Wang, Q-G., Zou, B., Lee, TH., Bi, Q. (1997). Auto-tuning of multivariable PID controllers from decentralized relay feedback. Automatica, 33(3):319–30.
- A. Alharin, T.-N. Doan, and M. Sartipi, “Reinforcement Learning Interpretation Methods: A Survey,” IEEE Access, vol. 8, pp. 171058–171077, 2020.
- T. M. Luu and C. D. Yoo, “Hindsight Goal Ranking on Replay Buffer for Sparse Reward Environment,” IEEE Access, vol. 9, pp. 51996–52007, Mar. 2021.
- Kiran, M. and Ozyildirim, M. Hyperparameter Tuning for Deep Reinforcement Learning Applications. arXiv: 2201.11182. (2022).
- Felten, F, Gareev, D, Talbi, E.G. and Danoy, G. Hyperparameter Optimization for Multi-Objective Reinforcement Learning. arXiv: 2310.16487.(2023).