Abstract
This paper introduces two modeling methods of the incinerator’s combustion control system. Aiming at the features of the control system which apply to the incinerator’s combustion system, this paper uses the techniques of system identification which are based on the least square method and the neural network, introduces two ways of model identification in the incinerator’s combustion control system, then they can be helpful for the following design of the control system. By simulating the model of the incinerator’s combustion control system, desired results can be obtained for following the track of the main control target-temperature.
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References
Lu GM. Research on fuzzy PID control system of temperature for electric boiler [Dissertation]. Heilongjiang: Harbin University of Science and Technology; 2007.
Sheng SB, Yuan LH. Cross control by the air-fuel in the burning control of the boiler. Therm Power Gen. 1998;4:49.
Zhou W, Lu HP, Zhang C, et al. The application of the double-cross-limiting ratio control in the temperature control of the reheating furnace. Metal Autom. 2011;2:434.
Li JT, Gao X, Zhang Q. The use of fuzzy control technique in water-coal starch boiler negative pressure of boiler’s chamber control system. China Instrument. 2006;6:73.
Qin P, Zhu J. Data hierarchical modeling algorithm for multi-input multi-output system. Mini-Micro Syst. 2003;24(1):76.
Yuan P. Comparisons and studies of identification methods for multivariable systems[Dissertation]. Jiangsu: Jiangnan University; 2008.
Wu B. Research of identification and pid control methods in multivariable system [Dissertation]. Beijing: Beijing University of Chemical Technology; 2008.
Yu KP, Dong HZ. Fast recursive least square scheme for time-varying nonlinear system identification base on feed forward neural network. In: The 9th vibration theory and application of academic conference. Zhejiang; 2007. p. 143.
Chen GC, Xie PF. Recognition and examine methods in the slither of Hydrology’s variation. J China Hydrol. 2006;26(2):57.
Chen CP, Cha YX, Qian P, et al. MapReduce-based BP Neural Network Genetic Algorithm to Study Nonlinear System Identification//The 18th National Youth Communication Conference. Beijing; 2013. p. 242.
Liu YM. The research of PID controller based on improved bp neural network [Dissertation]. Beijing: University of Chinese Academy of Sciences; 2012.
Yuan MH. System identification method study based on neural networks [Dissertation]. Beijing: Beijing University of Technology; 2008.
Wu JH, Wu JF, Li L. Nonlinear system identification based on BP neural network. In: The 12th annual conference of control and application, CSAA. Beijing; 2006 p. 73.
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Mu, Y., Fang, L., Liu, J. (2018). Simulation and Study of the Incinerator’s Combustion Control System. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2017 Chinese Intelligent Systems Conference. CISC 2017. Lecture Notes in Electrical Engineering, vol 460. Springer, Singapore. https://doi.org/10.1007/978-981-10-6499-9_43
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DOI: https://doi.org/10.1007/978-981-10-6499-9_43
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