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Research Article

Performance Analysis of Cascade Tank System Using Deep Learning Controller

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References

  • V. M. Chadeev, and N. I. Aristova, “Control of industrial automation,” Proc. 2017 10th Int. Conf. Manag. Large-Scale Syst. Dev. MLSD 2017 (2017. DOI: 10.1109/MLSD.2017.8109604.
  • E. L. Itskovich, “Methodology for attaining the rational level of plant automation,” Autom. Remote Control, Vol. 72, no. 5, pp. 1080–1088, 2011. DOI: 10.1134/S000511791105016X.
  • Predence Research. “Industrial automation and control systems market (By Component: HMI, Industrial Robots, Control Valves, Sensors, Others; By Control System: DCS, PLC, SCADA, Others; By Vertical: Aerospace & Défense, Automotive, Chemical, Energy & Utilities, Food & Beverag,” 2020. [Online]. Available: https://www.precedenceresearch.com/industrial-automation-and-control-systems-market.
  • N. Minorsky, “Directional stability of automatically steered bodies,” J. Am. Soc. Nav. Eng., Vol. 34, no. 2, pp. 280–309, 1922. DOI: 10.1111/j.1559-3584.1922.tb04958.x.
  • J. Zhong, “PID controller tuning: A short tutorial,” Springer, 1–13, 2006.
  • Y. Arya, “A new optimised fuzzy FOPI-FOPD controller for automatic generation control of electric power systems,” J. Franklin Inst., Vol. 356, no. 11, pp. 5611–5629, 2019. DOI: 10.1016/j.jfranklin.2019.02.034.
  • R. C. Roman, R. E. Precup, and E. M. Petriu, “Hybrid data-driven fuzzy active disturbance rejection control for tower crane systems,” Eur. J. Control, Vol. 58, pp. 373–387, 2021. DOI: 10.1016/j.ejcon.2020.08.001.
  • S. Chauhan, B. Singh, and M. Singh, “Modified ant colony optimisation based PID controller design for coupled tank system,” Eng. Res. Express, Vol. 3, no. 4, pp. 045005, 2021. DOI: 10.1088/2631-8695/ac2bf3.
  • R. Kumar, B. Singh, R. Kumar, and S. Marwaha, “Online identification of underlying causes for multiple and multi-stage power quality disturbances using S-transform,” IETE J. Res. 2021. DOI: 10.1080/03772063.2021.1913073.
  • B. Prasad, R. Kumar, and M. Singh, “Performance analysis of heat exchanger system using deep learning controller,” Int. J. Electr. Electron. Res., Vol. 10, no. 2, pp. 327–334, 2022. DOI: 10.37391/ijeer.100244.
  • B. Prasad, R. Kumar, and M. Singh, “Performance analysis of model predictive control for cascaded tank level control system,” IEEE 2nd International Conference on Electrical Power and Energy Systems, ICEPES 2021, 01–06, 2021. DOI: 10.1109/ICEPES52894.2021.9699765.
  • G. Rigatos, P. Siano, D. Selisteanu, and R. E. Precup, “Non-linear optimal control of oxygen and carbon dioxide levels in blood,” Intell. Ind. Syst., Vol. 3, no. 2, pp. 61–75, 2017. DOI: 10.1007/s40903-016-0060-y.
  • MathWorks. “Optimal Control.” https://in.mathworks.com/discovery/optimal-control.html.
  • P. Bernhard, and M. Deschamps, “Kalman 1960: The birth of modern system theory,” Math. Popul. Stud., Vol. 26, no. 3, pp. 123–145, 2019. DOI: 10.1080/08898480.2018.1553393.
  • J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Netw., Vol. 61, pp. 85–117, 2014. DOI: 10.1016/j.neunet.2014.09.003.
  • R. Chi, H. Li, D. Shen, Z. Hou, and B. Huang, “Enhanced P-type control: indirect adaptive learning from set-point updates,” IEEE Trans. Automat. Contr., Vol. 68, no. 3, pp. 1600–1613, 2023. DOI: 10.1109/TAC.2022.3154347.
  • P. J. Werbos, “Neural networks for control and system identification,” Proc. IEEE Conf. Decis. Control, Vol. 1, pp. 260–265, 1989. DOI: 10.1109/cdc.1989.70114.
  • N. A. S. Alwan, and Z. M. Hussain, “Deep learning control for digital feedback systems: improved performance with robustness against parameter change,” Electron., Vol. 10, no. 11, 2021. DOI: 10.3390/electronics10111245.
  • R. Kumar, B. Singh, D. T. Shahani, A. Chandra, and K. Al-Haddad, “Recognition of power-quality disturbances using S-transform-based ANN classifier and rule-based decision tree,” IEEE Trans. Ind. Appl., Vol. 51, no. 2, pp. 1249–1258, 2015. DOI: 10.1109/TIA.2014.2356639.
  • I. Alexandru Zamfirache, R. E. Precup, R. C. Roman, and E. M. Petriu, “Neural network-based control using actor-critic reinforcement learning and grey wolf optimizer with experimental servo system validation,” Expert Syst. Appl., Vol. 225, no. March, pp. 120112, 2023. DOI: 10.1016/j.eswa.2023.120112.
  • J. Hu, H. Niu, J. Carrasco, B. Lennox, and F. Arvin, “Voronoi-Based multi-robot autonomous exploration in unknown environments via deep reinforcement learning,” IEEE Trans. Veh. Technol., Vol. 69, no. 12, pp. 14413–14423, 2020. DOI: 10.1109/TVT.2020.3034800.
  • U. M, and J. S. Dan Ciresan. “Multi-column deep neural networks for image classificationtle”.
  • B. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Commun. ACM, Vol. 60, no. 6, pp. 84–90, 2012. DOI: 10.1145/3065386.
  • A. Kroll, and H. Schulte, “Benchmark problems for non-linear system identification and control using soft computing methods: need and overview,” Appl. Soft Comput., Vol. 25, pp. 496–513, 2014. DOI: 10.1016/j.asoc.2014.08.034.
  • H. A. Karaboğa, “Modeling mathematics achievement with deep learning methods,” Sigma J. Eng. Nat. Sci. – Sigma Mühendislik ve Fen Bilim. Derg., Vol. 39, no. 5, pp. 33–40, 2021. DOI: 10.14744/sigma.2021.00039.
  • V. Prasad, and B. W. Bequette, “Non-linear system identification and model reduction using artificial neural networks,” Comput. Chem. Eng., Vol. 27, no. 12, pp. 1741–1754, 2003. DOI: 10.1016/S0098-1354(03)00137-6.
  • D. gang Gao, Y. gang Sun, S. hui Luo, G. bin Lin, and L. sheng Tong, “Deep learning controller design of embedded control system for maglev train via deep belief network algorithm,” Des. Autom. Embed. Syst., Vol. 24, no. 3, pp. 161–181, 2020. DOI: 10.1007/s10617-020-09237-3.
  • S. N. Deepa, and N. Y. Jayalakshmi, “Optimised fuzzy-based wavelet neural network controller for a non-linear process control system,” IETE J. Res. 2021. DOI: 10.1080/03772063.2020.1865212.
  • A. Radu-Emil Precup, R.-C. Roman, and A. Safaei. Data-driven model-free controllers. CRC Press,  2021.
  • B. Prasad, R. Kumar, and M. Singh, “A comprehensive overview on performance of cascaded three tank level system using neural network predictive controller,” Int. J. Electr. Electron. Res., Vol. 11, no. 2, pp. 236–241, 2023. DOI: 10.37391/ijeer.110201.
  • G. Stephanopoulos. Chemical process control An introduction to theory and practice. 2015th ed. Pearson Education, 1984.
  • S. Skogestad, “Simple analytic rules for model reduction and PID controller tuning,” Model. Identif. Control, Vol. 25, no. 2, pp. 85–120, 2004. DOI: 10.4173/mic.2004.2.2.
  • P. Zhang. “Advanced industrial control technology British library cataloguing in publication data,” 2010.
  • Feedback Instruments Ltd. Coupled tanks control experiments. 2014.
  • C. Simon Haykin. McMaster University, Hamilton, Ontario, “Neural networks – A comprehensive foundation – Simon Haykin.pdf.” p. 823, 2005.
  • MathWorks. “Multilayer Shallow Neural Networks and Backpropagation Training.” https://in.mathworks.com/help/deeplearning/ug/multilayer-neural-networks-and-backpropagation-training.html.
  • M. Ben Nasr, and M. Chtourou, “Neural network control of non-linear dynamic systems using hybrid algorithm,” Appl. Soft Comput. J., Vol. 24, pp. 423–431, 2014. DOI: 10.1016/j.asoc.2014.07.023.
  • M. A. Hosen, et al., “NN-based Prediction interval for non-linear processes controller,” Int. J. Control. Autom. Syst., Vol. 19, no. 9, pp. 3239–3252, 2021. DOI: 10.1007/s12555-020-0342-8.

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