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Application of Model Free Adaptive Control in Main Steam Temperature System of Thermal Power Plant

Authors: Khaing Yadana Swe, Lillie Dewan


At present, the cascade PID control is widely used to control the superheating temperature (main steam temperature). As Main Steam Temperature has the characteristics of large inertia, large time-delay and time varying, etc., conventional PID control strategy cannot achieve good control performance. In order to overcome the bad performance and deficiencies of main steam temperature control system, Model Free Adaptive Control (MFAC) - P cascade control system is proposed in this paper. By substituting MFAC in PID of the main control loop of the main steam temperature control, it can overcome time delays, non-linearity, disturbance and time variation.

Keywords: Adaptive Control, Cascade Control, PID, Model free Adaptive Control

Digital Object Identifier (DOI):

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[1] Xi-Yun Yang, Xin-Ran Liu and Da-Ping Xu, “AFSMC -PID control for main steam temperature,” Proceedings of the 7th International Conference on Machine Learning and Cybernetics, Kunming, July 2008, pp. 1872-1876.
[2] Jianqiu Deng, Haijun Li and Zhengxia Zhang, “Main steam temperature control system based on smith-PID scheduling network control,” International Journal of Advanced Computer Science and Applications, Vol. 2, No. 5, 2011. pp. 54-58.
[3] Yue Zhang, “Application of grey self-tuning fuzzy immune PID control for main steam temperature control system,” Proceedings of the 8th International Conference on Machine Learning and Cybernetics, Baoding, July 2009, pp. 588-591
[4] Zhongsheng Hou, “The parameter identification, adaptive control and model free learning adaptive control for non-linear system,” Ph.D Thesis, North-easten University, Shengyang, 1994.
[5] Zhongsheng Hou and Shangtai Jin, “Model Free Adaptive Control : Theory and Application”, 1st edition, CRC Press. Boca Raton, US, 2013.
[6] Bu XH,, “Model free adaptive control algorithm with data dropout compensation”. Mathematical Problems in Engineering, 2012. pp. 1-14.
[7] Leandro dos Santos Coelho,, “Model-free adaptive control design using evolutionary-neural compensator,” Science Direct, Expert systems with Applications, vol. 37, issue 1, Aug 2010, pp. 499-508.
[8] F.L. Lv, S.B. Chen, and S.W. Dai, “A model-free adaptive control of pulsed GTAW,” Springer-Verlag Berlin Heidelberg, 2007, pp.333-339.
[9] Leandro dos Santos Coelho and Antonio Augusto Rodrigues Coelho, “Model-free adaptive control optimization using a chaotic particle swarm approach,” Chaos, Solitons and Fractals 41(2009), pp. 2001-2009.
[10] Wu Jianhua, Yang Haitao, Zhang Haixin and Zhu Mingguang, “Model-free adaptive control for model mismatch power converters,” in Control and Decision Conference (CCDC), China, May 2011, pp 1168-1171.
[11] Gao Qiang, “The study of model-free adaptive controller based on dSPACE,” IEEE Proceeding on Second International Symposium on Intelligent Information Technology Applications, 2008, pp. 608-611.
[12] Zhi-Gang Han and Xinghuo Yu, “An adaptive model free control design and its applications,” in International Conference on Industrial Imformatic., Harbin., PR China, 2004, pp.243-248.
[13] Ping MA,,’The application of Model free adaptive control.” IMACS Multi conference on computational Engineering in Systems Applications(CESA) Oct.2006, pp.393-396